1 Abstract

The muHVT package is a collection of R functions to facilitate building topology preserving maps for rich multivariate data analysis. Tending towards a big data preponderance, a large number of rows. A collection of R functions for this typical workflow is organized below:

Data Compression: Vector quantization (VQ), HVQ (hierarchical vector quantization) using means or medians. This step compresses the rows (long data frame) using a compression objective.
Data Projection: Dimension projection of the compressed cells to 1D,2D or 3D with the Sammons Non-linear Algorithm. This step creates topology preserving map (also called as embedding) coordinates into the desired output dimension.
Tessellation: Create cells required for object visualization using the Voronoi Tessellation method, package includes heatmap plots for hierarchical Voronoi tessellations (HVT). This step enables data insights, visualization, and interaction with the topology preserving map. Useful for semi-supervised tasks.
Prediction: Scoring new data sets and recording their assignment using the map objects from the above steps, in a sequence of maps if required.

X	price	speed	hd	ram	screen	cd	multi	premium	ads	trend
1	1499	25	80	4	14	no	no	yes	94	1
2	1795	33	85	2	14	no	no	yes	94	1
3	1595	25	170	4	15	no	no	yes	94	1
4	1849	25	170	8	14	no	no	no	94	1
5	3295	33	340	16	14	no	no	yes	94	1
6	3695	66	340	16	14	no	no	yes	94	1

Row.No	price	speed	hd	ram	screen	ads
2463	2799	50	230	8	15	216
2511	2197	33	270	4	14	216
2227	2744	50	340	8	17	275
526	2999	66	245	16	15	139
4291	1974	33	200	4	14	248
2986	2490	33	528	16	14	267

3 Map A : Base Compressed Map

Let us try to visualize the compressed Map A from the flow diagram below.

Figure 1: Flow map with highlighted bounding box in red around compressed map A

This package can perform vector quantization using the following algorithms -

Hierarchical Vector Quantization using k−means
Hierarchical Vector Quantization using k−medoids

For more information on vector quantization, refer the following link.

The HVT function constructs highly compressed hierarchical Voronoi tessellations. The raw data is first scaled and this scaled data is supplied as input to the vector quantization algorithm. The vector quantization algorithm compresses the dataset until a user-defined compression percentage/rate is achieved using a parameter called quantization error which acts as a threshold and determines the compression percentage. It means that for a given user-defined compression percentage we get the ‘n’ number of cells, then all of these cells formed will have a quantization error less than the threshold quantization error.

Let’s try to comprehend the HVT function first before moving ahead.

HVT(
  dataset,
  min_compression_perc,
  n_cells,
  depth,
  quant.err,
  distance_metric = c("L1_Norm", "L2_Norm"),
  error_metric = c("mean", "max"),
  quant_method = c("kmeans", "kmedoids"),
  normalize = TRUE,
  diagnose = FALSE,
  hvt_validation = FALSE,
  train_validation_split_ratio = 0.8
)

Each of the parameters have been explained below :

dataset - A dataframe with numeric columns.
min_compression_perc - An integer indicating the minimum percent compression rate to be achieved for the dataset.
n_cells - An integer indicating the number of cells per hierarchy (level).
depth - An integer indicating the number of levels. (1 = No hierarchy, 2 = 2 levels, etc …).
quant.error - A number indicating the quantization error threshold. A cell will only breakdown. into further cells if the quantization error of the cell is above the defined quantization error threshold.
projection.scale - A number indicating the scale factor for the tesselations so as to visualize the sub-tesselations well enough.
scale_summary - A list with mean and standard deviation values for all the features in the dataset. Pass the scale summary when the input dataset is already scaled or normalize is set to False.
distance_metric - The distance metric can be L1_Norm or L2_Norm. L1_Norm is selected by default. The distance metric is used to calculate the distance between an n dimensional point and centroid. The user can also pass a custom function to calculate this distance.
error_metric - The error metric can be mean or max. max is selected by default. max will return the max of m values and mean will take mean of m values where each value is a distance between a point and centroid of the cell. Moreover, the user can also pass a custom function to calculate the error metric.
quant_method - The quantization method can be kmeans or kmedoids. kmeans is selected by default.
normalize - A logical value indicating whether the columns in your dataset need to be normalized. Default value is TRUE. The algorithm supports Z-score normalization.
diagnose - A logical value indicating whether user wants to perform diagnostics on the model. Default value is TRUE.
hvt_validation - A logical value indicating whether user wants to holdout a validation set and find mean absolute deviation of the validation points from the centroid. Default value is FALSE.
train_validation_split_ratio - A numeric value indicating train validation split ratio. This argument is only used when hvt_validation has been set to TRUE. Default value for the argument is 0.8.

We will use the HVT function to compress our data while preserving essential features of the dataset. Our goal is to achieve data compression upto atleast 80%. In situations where the compression ratio does not meet the desired target, we can explore adjusting the model parameters as a potential solution. This involves making modifications to parameters such as the quantization error threshold or increasing the number of cells and then rerunning the HVT function again.

In our example we will iteratively increase the number of cells until the desired compression percentage is reached instead of increasing the quantization threshold because it may reduce the level of detail captured in the data representation

First, we will construct map A by using the below mentioned model parameters.

3.0.1 Iteration 1:

We will pass the below mentioned model parameters along with training dataset to HVT function.

Model Parameters

Number of Cells at each Level = 200
Maximum Depth = 1
Quantization Error Threshold = 0.2
Error Metric = Max
Distance Metric = Manhattan

set.seed(240)
map_A <- list()
map_A  <- muHVT::HVT(
  trainComputers,
  n_cells = 200,
  depth = 1,
  quant.err = 0.2,
  projection.scale = 10,
  normalize = T,
  distance_metric = "L1_Norm",
  error_metric = "max",
  quant_method = "kmeans"
)

Let’s checkout the compression summary.

compressionSummaryTable(map_A[[3]]$compression_summary)

segmentLevel	noOfCells	noOfCellsBelowQuantizationError	percentOfCellsBelowQuantizationErrorThreshold	parameters
1	200	83	0.42	n_cells: 200 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans

As it can be seen from the table above, 42% of cells have reached the quantization threshold error. Therefore we can further subdivide the cells by increasing the n_cells parameters and then see if desired compression (80%) is reached

3.0.2 Iteration 2:

Since, we are yet to achive atleast 80% compression. Let’s try to compress again using the below mentioned set of model parameters.

Model Parameters

Number of Cells at each Level = 440
Maximum Depth = 1
Quantization Error Threshold = 0.2
Error Metric = Max
Distance Metric = Manhattan

map_A <- list()
map_A <-muHVT::HVT(trainComputers,
                n_cells = 440,
                quant.err = 0.2,
                depth = 1,
                distance_metric = "L1_Norm",
                error_metric = "max",
                quant_method = "kmeans",
                normalize = T)

As per the manual, map_A[[3]] gives us detailed information about the hierarchical vector quantized data. map_A[[3]][['summary']] gives a nice tabular data containing no of points, Quantization Error and the codebook.

The datatable displayed below is the summary from map A

summaryTable(map_A[[3]]$summary,scroll = T,limit = 500)

Segment.Level	Segment.Parent	Segment.Child	n	Cell.ID	Quant.Error	price	speed	hd	ram	screen	ads
1	1	1	7	46	0.08	-0.76	-0.89	-0.88	-0.76	-0.67	1.57
1	1	2	10	108	0.08	-0.80	-0.89	-0.16	-0.76	-0.67	0.67
1	1	3	15	223	0.12	0.37	-0.89	-0.72	-0.05	0.43	-1.65
1	1	4	11	54	0.07	-1.50	-0.89	-0.75	-0.76	-0.67	0.62
1	1	5	8	146	0.13	-0.31	0.68	-0.95	-0.89	-0.67	-0.14
1	1	6	11	150	0.16	-0.66	0.68	-0.78	-0.79	-0.67	-0.73
1	1	7	11	170	0.1	0.03	-1.24	-0.13	-0.05	-0.67	0.38
1	1	8	8	334	0.15	0.62	2.30	0.08	-0.05	0.43	0.04
1	1	9	8	114	0.07	-0.16	0.68	-1.19	-1.11	-0.67	0.87
1	1	10	7	248	0.17	0.51	-0.08	0.34	-0.05	-0.67	-0.30
1	1	11	9	140	0.12	-0.01	0.68	-1.15	-1.00	-0.67	0.33
1	1	12	7	219	0.14	-1.36	0.24	0.46	-0.05	-0.67	-0.74
1	1	13	9	271	0.05	-1.08	0.68	0.49	-0.05	0.43	-0.84
1	1	14	19	109	0.06	-0.31	-0.89	-0.74	-0.76	-0.67	0.38
1	1	15	6	176	0.08	-0.72	-0.89	-0.07	-0.05	0.43	0.72
1	1	16	17	332	0.14	0.42	2.30	0.10	-0.05	0.43	1.50
1	1	17	12	18	0.05	-1.21	-1.27	-1.19	-1.11	-0.67	0.97
1	1	18	19	149	0.16	-0.68	-0.08	-0.46	-0.76	0.43	0.79
1	1	19	17	428	0.35	0.18	2.30	2.53	1.37	0.43	-2.22
1	1	20	20	320	0.36	0.82	-0.16	-0.09	-0.12	2.64	0.71
1	1	21	3	305	0.18	2.27	-0.35	-0.01	-0.05	-0.67	-1.32
1	1	22	7	227	0.1	-0.51	-0.89	0.45	-0.05	0.43	-0.47
1	1	23	10	178	0.12	0.00	0.68	-0.86	-0.76	-0.67	-0.90
1	1	24	9	365	0.1	0.68	-0.08	1.20	1.37	0.43	-0.36
1	1	25	5	14	0.11	-1.99	-0.89	-0.96	-1.11	-0.67	0.18
1	1	26	3	411	0.05	1.25	-0.89	2.29	2.79	0.43	0.57
1	1	27	18	122	0.15	-0.18	-0.98	-0.85	-0.76	0.43	0.68
1	1	28	15	189	0.11	0.40	-0.92	0.03	-0.05	-0.67	0.87
1	1	29	11	107	0.11	-0.49	-0.96	-0.88	-0.76	-0.67	-0.64
1	1	30	7	423	0.47	3.55	0.12	2.51	1.37	-0.67	0.44
1	1	31	14	90	0.05	-0.63	-0.89	-0.79	-0.76	-0.67	0.58
1	1	32	22	430	0.24	0.63	0.75	3.07	2.79	0.43	-2.27
1	1	33	5	390	0.3	1.37	-0.89	3.73	-0.19	-0.45	0.70
1	1	34	25	101	0.18	-0.85	-0.97	-0.71	-0.76	0.43	0.84
1	1	35	11	425	0.07	0.15	2.30	1.70	1.37	0.43	-2.39
1	1	36	10	358	0.05	0.24	-0.89	1.20	1.37	0.43	-0.84
1	1	37	16	166	0.11	0.03	0.68	-1.08	-0.78	-0.67	-1.65
1	1	38	13	45	0.05	-0.91	-0.89	-1.19	-1.11	-0.67	0.42
1	1	39	8	383	0.12	1.15	2.30	0.45	1.37	-0.67	-0.16
1	1	40	5	9	0.07	-1.24	-0.97	-1.19	-1.11	-0.67	1.57
1	1	41	11	419	0.06	1.41	-0.89	2.29	2.79	0.43	-0.82
1	1	42	8	242	0.14	-0.81	-0.08	0.30	-0.05	0.43	-0.65
1	1	43	13	179	0.09	0.41	0.68	-0.76	-0.76	-0.67	0.40
1	1	44	5	375	0.04	0.06	-0.08	1.70	1.37	0.43	-0.79
1	1	45	20	129	0.14	-1.15	0.68	-0.79	-0.76	-0.67	-0.40
1	1	46	10	292	0.22	0.86	0.68	-0.65	-0.12	0.43	-1.41
1	1	47	5	79	0.12	-0.89	-1.04	-0.94	-0.05	-0.67	1.02
1	1	48	23	246	0.11	-0.42	0.68	0.46	-0.05	-0.67	-0.63
1	1	49	8	207	0.25	0.74	-0.89	-0.40	-0.40	0.43	0.52
1	1	50	11	27	0.06	-1.06	-1.27	-1.19	-1.11	-0.67	0.43
1	1	51	8	51	0.11	-1.23	-0.08	-1.05	-0.85	-0.67	0.94
1	1	52	19	288	0.09	0.81	-0.89	0.45	1.37	-0.67	0.88
1	1	53	7	154	0.1	-0.62	0.68	-0.15	-0.76	-0.67	0.84
1	1	54	10	261	0.15	0.61	-0.08	-0.67	-0.05	0.43	-1.40
1	1	55	10	195	0.15	0.18	0.77	-0.09	-0.76	-0.67	0.83
1	1	56	14	250	0.09	0.52	0.68	-0.69	-0.05	-0.67	-1.61
1	1	57	20	331	0.2	-0.63	0.68	0.30	-0.76	2.64	-0.95
1	1	58	9	379	0.15	1.33	-0.08	-0.65	-0.29	2.64	-1.52
1	1	59	14	11	0.21	-0.28	-1.05	-0.79	-0.76	2.64	0.53
1	1	60	29	359	0.13	1.36	0.68	0.18	1.37	0.43	0.77
1	1	61	6	337	0.1	2.46	0.68	0.21	-0.05	-0.67	-0.87
1	1	62	6	1	0.17	-0.17	-1.21	-1.02	-0.76	2.64	1.32
1	1	63	28	243	0.33	-0.33	2.30	-0.23	-0.46	-0.67	-0.88
1	1	64	8	274	0.07	0.41	-1.27	0.45	1.37	-0.67	0.68
1	1	65	13	362	0.14	1.07	0.75	0.35	1.37	0.43	1.34
1	1	66	10	143	0.07	-0.34	-0.89	-0.80	-0.05	-0.67	-1.66
1	1	67	4	265	0.05	-0.55	0.68	1.23	-0.05	-0.67	-0.69
1	1	68	11	13	0.15	-0.83	-0.89	-0.25	-0.76	2.64	-0.33
1	1	69	8	298	0.17	-0.62	0.20	2.29	-0.05	-0.67	-0.95
1	1	70	4	335	0.06	1.34	-0.08	0.45	1.37	-0.67	-0.08
1	1	71	20	204	0.16	0.09	-0.08	0.02	-0.05	-0.67	0.86
1	1	72	10	42	0.06	-1.49	-0.89	-0.75	-0.76	-0.67	1.04
1	1	73	1	429	0	3.08	0.68	0.04	4.20	0.43	0.71
1	1	74	14	186	0.14	-0.79	-0.89	0.45	-0.05	-0.67	-0.68
1	1	75	4	410	0.37	2.27	0.68	3.73	-0.23	-0.40	0.68
1	1	76	9	163	0.16	1.05	-0.89	-0.41	-0.60	-0.67	0.61
1	1	77	10	400	0.07	-0.03	0.68	1.70	1.37	0.43	-2.38
1	1	78	6	275	0.18	1.14	0.68	0.13	-0.05	-0.67	-0.18
1	1	79	25	241	0.16	-0.88	0.68	0.40	-0.05	-0.67	-1.06
1	1	80	6	245	0.14	-1.22	0.68	-0.30	-0.05	0.43	-0.91
1	1	81	21	120	0.16	-0.46	-0.08	-0.82	-0.62	-0.67	0.70
1	1	82	11	40	0.18	-0.93	-0.99	-1.19	-1.11	0.43	0.37
1	1	83	9	342	0.28	1.16	0.43	-0.52	-0.68	2.64	1.12
1	1	84	8	286	0.05	-0.72	1.11	0.50	-0.05	0.43	-0.83
1	1	85	8	33	0.1	-1.06	-0.89	-1.18	-1.02	-0.67	-0.99
1	1	86	5	282	0.23	-1.50	0.53	0.19	-0.48	0.43	-2.16
1	1	87	17	137	0.07	-0.31	0.68	-0.78	-0.76	-0.67	0.96
1	1	88	19	168	0.07	-0.08	-0.89	0.05	-0.05	-0.67	1.04
1	1	89	7	291	0.05	1.10	-0.89	0.15	1.37	-0.67	0.36
1	1	90	6	24	0.19	-0.97	-1.02	-1.07	-0.94	0.43	-1.40
1	1	91	4	434	0.59	4.07	1.49	1.29	0.30	2.64	0.07
1	1	92	19	409	0.65	0.92	0.42	1.41	1.37	2.64	-0.60
1	1	93	9	393	0.46	2.04	2.30	0.91	-0.05	-0.31	-0.34
1	1	94	8	22	0.08	-1.66	-1.27	-0.84	-0.76	-0.67	0.84
1	1	95	23	158	0.22	-1.41	0.68	-0.20	-0.70	-0.67	-1.05
1	1	96	11	330	0.22	1.55	0.68	-0.45	-0.31	0.43	-1.59
1	1	97	6	145	0.17	0.49	-0.89	-0.64	-0.76	-0.67	-0.17
1	1	98	12	121	0.15	0.21	-0.89	-0.80	-0.76	-0.67	-1.70
1	1	99	14	329	0.24	2.06	0.63	0.31	-0.25	0.43	0.99
1	1	100	16	299	0.05	1.21	-0.89	0.46	1.37	-0.67	0.86
1	1	101	5	328	0.1	-0.87	1.11	1.33	-0.05	0.43	-1.13
1	1	102	10	278	0.1	0.33	-0.93	0.45	1.37	-0.67	0.02
1	1	103	5	102	0.1	-1.05	0.68	-0.76	-0.76	-0.67	1.27
1	1	104	12	25	0.08	-1.25	-1.05	-0.78	-0.76	-0.67	1.57
1	1	105	9	385	0.25	2.05	0.34	-0.21	-0.05	2.64	1.12
1	1	106	10	231	0.18	-0.02	-0.08	0.02	-0.05	0.43	1.29
1	1	107	5	193	0.14	-0.66	-0.08	-0.33	-0.05	-0.67	-1.01
1	1	108	4	418	0.06	-0.03	0.68	3.07	1.37	0.43	-2.25
1	1	109	4	306	0.16	1.83	0.68	-0.59	-0.05	-0.67	-1.57
1	1	110	5	378	0.15	1.09	0.68	1.20	1.37	0.43	-0.11
1	1	111	2	308	0.07	-0.55	0.68	0.26	1.37	-0.67	-1.25
1	1	112	18	239	0.15	0.44	0.75	0.08	-0.05	-0.67	1.05
1	1	113	11	62	0.06	-1.14	-0.89	-0.81	-0.76	-0.67	0.73
1	1	114	5	169	0.07	-0.26	-0.89	0.47	-0.05	-0.67	1.40
1	1	115	18	415	0.11	0.74	-0.08	2.29	2.79	0.43	-0.93
1	1	116	5	97	0.13	0.23	-1.19	-1.05	-0.76	-0.67	0.77
1	1	117	24	19	0.22	-0.06	2.30	-0.89	-0.89	-0.67	1.18
1	1	118	9	184	0.17	-0.98	0.34	-0.29	-0.05	-0.67	-0.08
1	1	119	11	43	0.12	-0.82	-0.89	-1.04	-0.79	-0.67	-1.65
1	1	120	9	257	0.29	2.17	-0.62	0.10	-0.05	-0.55	0.65
1	1	121	27	155	0.18	0.15	0.68	-0.84	-0.76	-0.67	0.81
1	1	122	20	215	0.11	0.33	-0.08	-0.69	-0.05	-0.67	-1.64
1	1	123	14	348	0.24	0.61	0.74	0.37	1.37	0.43	0.25
1	1	124	13	162	0.12	0.01	-1.01	-0.67	-0.05	-0.67	-1.57
1	1	125	7	366	0.22	1.62	0.35	-0.29	1.37	-0.67	-1.65
1	1	126	9	77	0.02	-0.88	-0.89	-0.78	-0.76	-0.67	0.66
1	1	127	25	253	0.14	0.70	0.68	0.16	-0.05	-0.67	0.61
1	1	128	9	84	0.09	-0.27	-1.27	-0.81	-0.76	-0.67	0.81
1	1	129	3	398	0.04	-0.03	-0.08	2.29	1.37	0.43	-1.98
1	1	130	9	412	0.05	0.90	-0.89	2.29	2.79	0.43	-0.48
1	1	131	8	309	0.06	1.43	-0.89	0.41	1.37	-0.67	0.46
1	1	132	8	267	0.08	0.10	0.68	0.37	-0.05	0.43	1.57
1	1	133	16	252	0.22	0.94	-0.08	-0.40	-0.14	0.43	0.43
1	1	134	15	119	0.13	-0.60	-0.99	-0.75	-0.76	0.43	0.24
1	1	135	13	48	0.09	-0.66	-0.92	-1.19	-1.11	-0.67	0.82
1	1	136	9	326	0.11	1.45	-0.08	0.32	1.37	-0.67	0.38
1	1	137	11	386	0.22	1.37	0.68	-0.66	-0.18	2.64	-1.36
1	1	138	20	255	0.15	0.43	-0.89	-0.34	-0.05	2.64	0.68
1	1	139	8	161	0.14	-0.68	-0.94	-0.08	-0.05	0.43	1.41
1	1	140	10	38	0.13	-1.22	-1.19	-0.92	-0.76	0.43	0.91
1	1	141	14	180	0.12	0.09	0.68	-0.56	-0.76	-0.67	0.07
1	1	142	13	427	0.21	2.01	0.62	2.29	2.79	0.43	-0.18
1	1	143	15	354	0.13	1.63	0.68	0.39	1.37	-0.67	0.26
1	1	144	11	301	0.14	0.82	-1.03	0.15	1.37	-0.67	-0.85
1	1	145	7	205	0.13	0.24	-0.08	-0.05	-0.05	-0.67	1.57
1	1	146	10	327	0.14	0.66	0.68	0.60	1.37	-0.67	0.42
1	1	147	11	403	0.11	0.92	2.30	1.22	1.37	0.43	-0.79
1	1	148	7	127	0.08	-0.20	-0.89	-0.10	-0.76	-0.67	0.69
1	1	149	6	217	0.11	0.41	-0.89	0.26	-0.05	-0.67	-0.34
1	1	150	11	323	0.1	0.96	-0.89	0.46	1.37	0.43	0.72
1	1	151	9	208	0.16	0.16	0.68	-0.60	-0.05	-0.67	0.43
1	1	152	14	405	0.14	-0.04	0.68	2.29	1.37	0.43	-2.24
1	1	153	10	98	0.08	-0.22	-0.89	-1.14	-0.76	-0.67	0.33
1	1	154	7	47	0.05	-0.99	-0.89	-1.16	-0.76	-0.67	1.03
1	1	155	8	182	0.15	-1.00	0.68	0.08	-0.76	-0.67	-0.46
1	1	156	9	153	0.07	-0.24	-0.93	0.04	-0.05	-0.67	1.57
1	1	157	23	65	0.08	-1.23	-0.89	-0.82	-0.76	-0.67	0.27
1	1	158	4	367	0.08	3.03	-0.08	0.15	-0.05	-0.67	-1.65
1	1	159	29	421	0.15	1.04	0.68	2.29	2.79	0.43	-0.84
1	1	160	10	111	0.14	-1.33	-0.89	0.06	-0.76	-0.67	-0.65
1	1	161	13	382	0.25	3.03	0.68	0.23	-0.05	-0.42	-1.68
1	1	162	10	142	0.1	-0.54	-0.89	-0.76	-0.05	-0.67	-0.73
1	1	163	14	433	0.32	1.31	-0.10	2.29	2.79	2.64	-0.81
1	1	164	14	50	0.08	-0.88	-0.08	-1.19	-1.11	-0.67	0.91
1	1	165	23	125	0.14	-0.78	0.68	-0.84	-0.76	-0.67	0.41
1	1	166	7	67	0.09	-0.95	0.68	-1.13	-0.96	-0.67	1.03
1	1	167	12	254	0.11	-0.53	0.68	0.02	-0.05	0.43	-0.24
1	1	168	11	113	0.17	-0.63	-0.08	-0.98	-0.92	-0.67	-0.30
1	1	169	9	83	0.04	-0.71	-0.89	-0.75	-0.76	-0.67	0.76
1	1	170	10	89	0.11	-1.18	-0.93	-0.19	-0.76	-0.67	0.66
1	1	171	8	44	0.06	-1.41	-0.89	-1.13	-0.76	-0.67	0.36
1	1	172	8	310	0.08	1.13	-0.99	0.45	1.37	-0.67	-0.08
1	1	173	8	6	0.08	-1.21	-0.89	-1.28	-1.11	-0.67	-1.64
1	1	174	14	295	0.12	-0.78	-0.08	0.38	-0.76	2.64	-1.00
1	1	175	12	316	0.19	1.00	-1.02	-0.29	1.37	-0.67	-1.61
1	1	176	2	346	0.15	0.38	-0.11	2.60	-0.05	0.43	0.69
1	1	177	12	194	0.17	-0.92	0.68	-0.46	-0.76	0.43	-0.16
1	1	178	17	396	0.19	1.51	2.30	0.45	1.37	-0.09	1.34
1	1	179	15	206	0.13	-1.30	-0.08	0.26	-0.76	0.43	-0.90
1	1	180	4	322	0.13	1.35	-0.89	-0.06	1.37	0.43	0.52
1	1	181	31	225	0.12	-0.25	0.68	0.14	-0.05	-0.67	0.21
1	1	182	5	185	0.14	0.46	-0.57	-0.29	-0.76	0.43	1.27
1	1	183	12	103	0.15	-0.68	-0.08	-0.63	-0.76	-0.67	1.39
1	1	184	2	151	0.06	-0.29	-0.89	0.06	-0.76	-0.67	-0.87
1	1	185	11	232	0.12	-0.39	0.68	0.22	-0.05	-0.67	-0.20
1	1	186	9	313	0.29	0.41	-0.89	1.32	1.37	-0.67	0.31
1	1	187	17	372	0.15	0.23	0.70	1.21	1.37	0.43	-0.74
1	1	188	9	407	0.09	0.41	2.30	1.70	1.37	0.43	-1.08
1	1	189	14	68	0.36	-0.83	0.19	-1.16	-1.09	0.43	0.95
1	1	190	9	37	0.08	-1.53	-1.27	-0.83	-0.76	-0.67	0.41
1	1	191	8	28	0.13	-1.33	-1.22	-1.10	-0.85	-0.67	-0.62
1	1	192	8	71	0.24	-1.65	-0.89	-0.19	-0.67	-0.67	0.43
1	1	193	9	7	0.2	-0.89	-0.89	-0.36	-0.84	2.64	0.50
1	1	194	16	53	0.05	-1.15	-0.89	-0.82	-0.76	-0.67	1.06
1	1	195	9	29	0.07	-0.60	-1.27	-1.08	-0.76	-0.67	-1.66
1	1	196	14	377	0.21	2.10	0.68	0.25	1.37	0.43	0.77
1	1	197	10	190	0.2	-0.45	-0.73	-0.74	-0.05	0.43	-0.77
1	1	198	11	15	0.13	-1.32	-1.03	-1.19	-1.11	0.43	0.88
1	1	199	13	344	0.23	0.11	0.68	0.14	-0.05	2.64	-0.39
1	1	200	12	128	0.1	0.05	-0.08	-0.81	-0.76	-0.67	0.90
1	1	201	17	36	0.06	-1.25	-1.27	-1.13	-0.76	-0.67	0.40
1	1	202	12	172	0.1	-0.33	-0.89	-0.44	-0.05	0.43	0.94
1	1	203	8	293	0.04	-0.74	1.11	0.50	-0.05	0.43	-1.22
1	1	204	4	165	0.06	0.02	-0.89	-0.67	-0.76	0.43	-1.69
1	1	205	13	187	0.13	0.43	-0.89	-0.71	-0.05	-0.67	-1.63
1	1	206	4	210	0.13	0.00	2.30	-0.78	-0.76	0.43	0.94
1	1	207	12	70	0.05	-0.72	-1.27	-0.81	-0.76	-0.67	0.69
1	1	208	13	156	0.14	0.14	-0.08	-0.21	-0.76	-0.67	0.93
1	1	209	10	317	0.17	0.87	2.30	0.21	-0.05	-0.67	1.31
1	1	210	19	197	0.23	-0.12	0.68	-0.79	-0.83	0.43	0.37
1	1	211	13	201	0.15	0.33	-0.98	-0.29	-0.05	-0.67	-0.85
1	1	212	12	94	0.15	-1.25	-0.08	-0.80	-0.76	-0.67	0.38
1	1	213	26	85	0.09	-0.35	-0.89	-0.98	-0.76	-0.67	-1.63
1	1	214	13	324	0.11	0.51	2.30	0.21	-0.05	0.43	0.66
1	1	215	9	95	0.08	-0.54	0.68	-1.18	-1.07	-0.67	1.01
1	1	216	23	126	0.14	-0.21	-0.08	-1.01	-0.77	-0.67	-1.64
1	1	217	10	256	0.2	0.88	0.68	-0.39	-0.05	-0.67	-0.79
1	1	218	19	356	0.34	-0.96	0.52	1.97	-0.20	-0.67	-2.18
1	1	219	14	147	0.11	0.17	-0.08	-0.77	-0.76	-0.67	0.27
1	1	220	14	240	0.2	0.44	0.86	0.03	-0.10	-0.67	1.57
1	1	221	13	191	0.18	0.14	-0.08	-0.75	-0.49	0.43	0.67
1	1	222	7	380	0.05	0.04	-0.08	1.70	1.37	0.43	-1.23
1	1	223	18	88	0.05	-0.46	-0.89	-0.77	-0.76	-0.67	1.00
1	1	224	12	417	0.1	1.69	0.68	2.29	2.79	-0.67	0.38
1	1	225	18	413	0.27	2.56	0.68	-0.04	1.37	2.64	0.59
1	1	226	9	401	0.08	0.81	-0.89	1.20	1.37	2.64	-0.61
1	1	227	8	49	0.08	-1.28	-0.89	-0.29	-0.76	-0.67	1.41
1	1	228	8	12	0.18	-1.32	-1.03	-0.93	-0.85	0.43	1.57
1	1	229	6	237	0.19	0.49	0.17	-0.73	-0.76	0.43	-1.52
1	1	230	16	266	0.12	-0.03	0.68	0.49	-0.05	0.43	0.54
1	1	231	5	82	0.12	-1.25	-1.12	-0.29	-0.05	-0.67	1.27
1	1	232	24	110	0.12	-0.42	-1.00	-0.71	-0.76	-0.67	-0.07
1	1	233	14	270	0.15	0.34	-1.11	0.45	1.37	-0.67	1.39
1	1	234	4	200	0.07	-0.30	-0.89	0.26	-0.05	0.43	1.57
1	1	235	16	263	0.29	0.70	0.35	-0.52	-0.18	0.43	-0.62
1	1	236	12	280	0.21	1.35	0.68	-0.41	-0.23	0.43	0.90
1	1	237	8	281	0.07	-0.96	0.68	0.48	-0.05	0.43	-1.23
1	1	238	7	287	0.13	0.20	0.68	0.45	-0.05	0.43	-0.55
1	1	239	16	116	0.18	-1.23	-0.08	-0.57	-0.76	-0.67	-0.37
1	1	240	31	321	0.42	-0.50	2.30	0.40	-0.12	-0.03	-1.00
1	1	241	16	420	0.13	1.51	-0.08	2.29	2.79	0.43	-0.49
1	1	242	8	199	0.12	0.05	-1.03	0.20	-0.05	-0.67	-0.07
1	1	243	8	314	0.07	0.99	-0.08	0.45	1.37	-0.67	0.72
1	1	244	7	289	0.06	-0.20	0.68	1.23	-0.05	0.43	0.35
1	1	245	15	216	0.16	-0.16	0.68	0.03	-0.05	-0.67	0.76
1	1	246	14	273	0.16	0.31	0.63	0.14	-0.05	0.43	0.07
1	1	247	20	132	0.18	-0.76	-0.08	-0.14	-0.72	-0.67	0.74
1	1	248	5	369	0.08	-0.74	0.68	1.70	-0.05	0.43	-2.35
1	1	249	11	432	0.33	0.40	1.35	2.13	1.37	2.64	-2.28
1	1	250	6	341	0.22	0.46	0.62	0.21	1.37	0.43	1.36
1	1	251	24	404	0.12	1.30	-0.89	2.29	2.79	-0.67	0.33
1	1	252	9	63	0.05	-1.23	-0.89	-0.75	-0.76	-0.67	0.65
1	1	253	11	226	0.16	0.32	-0.96	-0.44	-0.05	0.43	-0.89
1	1	254	11	133	0.09	-0.43	-0.89	-0.75	-0.05	-0.67	0.33
1	1	255	19	376	0.16	0.60	0.68	1.17	1.37	0.43	-0.70
1	1	256	8	80	0.06	-0.23	-0.89	-1.15	-0.76	-0.67	0.91
1	1	257	13	164	0.27	-0.58	0.15	-0.89	-0.87	0.43	-0.58
1	1	258	7	78	0.07	-0.82	-0.89	-0.29	-0.76	-0.67	1.33
1	1	259	5	422	0.05	1.83	-0.89	2.29	2.79	0.43	-0.23
1	1	260	10	23	0.11	-1.45	-1.23	-1.16	-0.83	-0.67	-0.10
1	1	261	8	60	0.05	-0.57	-1.27	-1.14	-0.76	-0.67	0.44
1	1	262	12	93	0.12	-0.88	-0.92	-0.78	-0.76	-0.67	-0.41
1	1	263	20	279	0.24	0.33	2.30	0.14	-0.19	-0.67	0.31
1	1	264	15	75	0.05	-0.67	-0.89	-1.10	-0.76	-0.67	0.39
1	1	265	15	351	0.04	0.27	-0.89	1.20	1.37	0.43	-0.45
1	1	266	7	408	0.25	2.71	-0.89	-0.27	1.37	2.64	0.06
1	1	267	10	57	0.07	-0.55	-1.27	-1.15	-0.76	-0.67	0.82
1	1	268	13	387	0.24	0.11	0.68	1.48	-0.05	2.64	-0.61
1	1	269	6	436	0.27	1.23	2.30	1.91	2.08	2.64	-1.04
1	1	270	11	202	0.09	0.20	-0.89	0.47	-0.05	-0.67	0.68
1	1	271	12	402	0.47	1.54	0.81	2.42	1.37	0.34	0.54
1	1	272	12	152	0.15	-0.05	-0.08	-0.83	-0.76	-0.67	-0.87
1	1	273	9	21	0.09	-1.48	-0.89	-1.15	-0.96	-0.67	0.95
1	1	274	11	69	0.05	-0.74	-0.89	-0.84	-0.76	-0.67	1.11
1	1	275	14	249	0.24	-0.06	-0.77	-0.01	-0.15	2.64	0.62
1	1	276	4	318	0.08	-1.02	0.68	1.35	-0.05	0.43	-1.21
1	1	277	19	247	0.15	0.09	0.68	-0.36	-0.05	0.43	0.65
1	1	278	14	297	0.17	1.23	0.68	0.23	-0.05	0.43	0.58
1	1	279	8	338	0.08	0.88	0.68	0.45	1.37	-0.67	1.41
1	1	280	13	171	0.1	-0.38	-0.92	0.00	-0.05	-0.67	0.06
1	1	281	14	371	0.28	-0.93	2.30	0.46	-0.20	-0.12	-2.31
1	1	282	24	173	0.33	-0.28	0.73	-0.75	-0.82	0.43	0.97
1	1	283	18	355	0.14	1.38	0.68	-0.21	1.37	0.43	0.42
1	1	284	4	345	0.11	0.83	-0.49	-0.66	1.37	0.43	-1.59
1	1	285	11	56	0.05	-0.83	-1.27	-0.78	-0.76	-0.67	1.01
1	1	286	7	370	0.19	1.55	0.68	-0.38	1.37	0.43	-1.02
1	1	287	10	64	0.06	-0.48	-0.89	-1.19	-1.11	-0.67	0.41
1	1	288	9	167	0.15	-0.36	-0.89	-0.71	-0.05	0.43	0.29
1	1	289	6	124	0.15	-1.28	-1.02	-0.29	-0.05	-0.67	-0.30
1	1	290	14	272	0.11	-0.70	0.68	0.43	-0.05	0.43	-0.78
1	1	291	25	188	0.09	0.21	-0.89	0.05	-0.05	-0.67	0.52
1	1	292	23	435	0.3	1.05	2.30	2.59	2.79	0.43	-1.56
1	1	293	8	8	0.08	-1.66	-1.27	-1.18	-0.94	-0.67	1.07
1	1	294	14	431	0.2	2.14	2.30	2.29	2.79	-0.60	0.40
1	1	295	3	96	0.08	-0.16	-0.89	-0.58	-0.76	-0.67	1.57
1	1	296	9	87	0.06	-0.46	-1.27	-0.78	-0.76	-0.67	0.39
1	1	297	6	414	0.04	0.90	-0.89	2.29	2.79	0.43	-0.85
1	1	298	10	230	0.18	0.31	-0.89	0.32	-0.12	0.43	0.71
1	1	299	13	58	0.11	-0.84	-0.95	-1.16	-0.98	-0.67	-0.08
1	1	300	11	4	0.31	0.63	-1.03	-0.85	-0.76	2.64	0.96
1	1	301	24	277	0.18	0.59	0.68	0.21	-0.08	0.43	0.76
1	1	302	13	130	0.1	-0.85	-0.08	-0.61	-0.76	-0.67	-0.80
1	1	303	6	131	0.17	-0.53	0.68	-0.61	-0.52	-0.67	1.57
1	1	304	10	55	0.09	-1.09	-1.27	-0.81	-0.76	-0.67	0.53
1	1	305	21	269	0.11	-1.16	0.68	0.48	-0.05	-0.67	-2.25
1	1	306	21	91	0.1	-0.45	-0.08	-1.19	-1.11	-0.67	0.57
1	1	307	9	104	0.08	-0.46	-0.98	-0.87	-0.76	-0.67	-1.12
1	1	308	19	32	0.07	-1.02	-0.89	-1.19	-1.11	-0.67	0.97
1	1	309	4	268	0.13	0.22	-0.89	0.07	-0.05	2.64	1.46
1	1	310	4	59	0.03	-0.93	-0.89	-1.14	-0.76	-0.67	0.69
1	1	311	4	2	0.12	-0.52	-0.89	-0.66	-0.76	2.64	1.22
1	1	312	8	3	0.15	-1.66	-1.22	-1.08	-0.89	-0.67	1.57
1	1	313	8	141	0.14	-0.61	-0.89	-0.21	-0.76	0.43	0.62
1	1	314	14	183	0.08	-0.35	-0.89	0.45	-0.05	-0.67	0.46
1	1	315	6	192	0.15	0.62	-0.89	-0.58	-0.76	0.43	-0.26
1	1	316	6	196	0.17	-0.81	-0.76	-0.07	-0.05	0.43	0.02
1	1	317	15	92	0.04	-0.75	-0.89	-0.75	-0.76	-0.67	0.36
1	1	318	6	214	0.21	1.06	0.17	-0.90	-0.64	-0.67	-1.66
1	1	319	9	389	0.05	0.14	0.68	1.70	1.37	0.43	-1.23
1	1	320	8	381	0.26	-0.31	2.30	1.86	-0.05	-0.12	-0.99
1	1	321	2	438	0.38	2.15	1.49	6.57	1.37	0.43	-0.28
1	1	322	18	224	0.09	-0.62	0.68	0.03	-0.05	-0.67	-0.65
1	1	323	7	361	0.05	0.63	-0.89	1.20	1.37	0.43	-0.72
1	1	324	8	352	0.12	0.50	-0.89	1.20	1.37	0.43	-0.08
1	1	325	8	234	0.1	-0.43	0.68	-0.12	-0.05	0.43	1.46
1	1	326	4	17	0.04	-1.04	-0.08	-1.19	-1.11	-0.67	1.57
1	1	327	3	5	0.04	-2.07	-0.89	-1.18	-1.11	-0.67	0.90
1	1	328	9	304	0.1	-0.22	0.68	1.23	-0.05	0.43	-0.46
1	1	329	9	350	0.09	-1.17	2.30	0.45	-0.76	-0.67	-2.26
1	1	330	12	294	0.1	0.98	-0.92	0.45	1.37	-0.67	0.42
1	1	331	9	307	0.16	0.42	-1.14	0.45	1.37	0.43	1.02
1	1	332	9	439	0.49	1.48	1.28	4.62	3.89	0.92	-2.46
1	1	333	10	148	0.16	-1.03	-0.57	-0.22	-0.76	0.43	-0.12
1	1	334	6	424	0.25	1.52	2.30	0.32	1.37	2.64	0.77
1	1	335	19	52	0.13	-1.61	-0.91	-0.75	-0.78	-0.67	-0.31
1	1	336	12	31	0.05	-1.28	-0.89	-1.19	-1.11	-0.67	0.60
1	1	337	19	123	0.2	-1.52	-0.08	-0.20	-0.76	-0.67	-0.94
1	1	338	11	218	0.17	-1.37	0.54	0.45	-0.76	-0.67	-2.18
1	1	339	18	262	0.2	0.88	0.68	-0.51	-0.17	0.43	0.51
1	1	340	3	325	0.07	-1.02	0.68	0.58	-0.05	0.43	-2.46
1	1	341	7	212	0.1	-1.39	0.68	-0.26	-0.76	0.43	-1.09
1	1	342	12	41	0.1	-1.75	-0.92	-0.62	-0.76	-0.67	-0.88
1	1	343	7	290	0.08	0.87	-1.27	0.46	1.37	-0.67	0.86
1	1	344	8	139	0.12	-1.50	-0.89	0.01	-0.76	0.43	-0.63
1	1	345	14	360	0.27	2.68	0.68	0.44	-0.25	0.43	0.22
1	1	346	6	181	0.05	-0.41	-0.89	-0.29	-0.05	0.43	0.55
1	1	347	13	353	0.11	1.54	0.68	0.47	1.37	-0.67	0.87
1	1	348	8	349	0.13	1.34	0.30	0.15	1.37	-0.67	-0.87
1	1	349	6	61	0.04	-0.65	-0.89	-1.14	-0.76	-0.67	0.94
1	1	350	21	336	0.25	0.45	0.68	0.04	-0.12	2.64	0.86
1	1	351	12	235	0.26	-0.42	0.36	-0.39	-0.76	2.64	0.12
1	1	352	10	115	0.16	-1.06	-1.08	-0.38	-0.05	-0.67	0.60
1	1	353	5	339	0.15	-0.28	0.22	2.29	-0.05	0.43	-0.55
1	1	354	16	397	0.34	-0.72	2.30	1.86	-0.14	-0.26	-2.30
1	1	355	4	236	0.15	-0.45	0.68	-0.08	-0.05	-0.67	-1.43
1	1	356	11	160	0.1	-0.31	-1.27	0.12	-0.05	-0.67	0.58
1	1	357	15	10	0.12	-1.28	-1.27	-1.05	-0.83	-0.67	-1.64
1	1	358	4	392	0.2	0.97	2.30	0.54	1.37	0.43	0.28
1	1	359	10	144	0.11	0.46	-0.89	-0.29	-0.76	-0.67	0.75
1	1	360	7	213	0.07	-0.32	-0.89	0.48	-0.05	0.43	0.59
1	1	361	9	394	0.28	2.75	0.68	-0.09	-0.29	2.64	0.44
1	1	362	7	340	0.2	0.97	-1.00	0.03	1.37	0.43	-0.83
1	1	363	21	251	0.24	1.15	0.68	-0.21	-0.12	-0.67	0.79
1	1	364	11	118	0.1	0.14	-0.89	-0.84	-0.76	-0.67	0.42
1	1	365	12	99	0.04	-0.35	-0.89	-0.79	-0.76	-0.67	0.76
1	1	366	8	388	0.15	1.15	2.30	0.73	1.37	-0.67	0.52
1	1	367	8	260	0.14	0.41	0.68	0.40	-0.05	-0.67	-0.36
1	1	368	12	117	0.09	-0.93	-0.89	-0.04	-0.76	-0.67	0.01
1	1	369	12	303	0.05	-0.52	0.68	1.23	-0.05	0.43	-0.83
1	1	370	7	395	0.21	1.06	2.30	0.16	-0.05	2.64	0.75
1	1	371	17	81	0.13	-1.49	-0.91	-0.30	-0.76	-0.67	-0.20
1	1	372	10	209	0.28	0.96	0.60	-0.64	-0.76	-0.67	-0.04
1	1	373	17	66	0.08	-1.33	-0.89	-0.81	-0.76	-0.67	-0.51
1	1	374	14	136	0.12	-0.28	-0.92	-0.50	-0.05	-0.67	1.05
1	1	375	20	198	0.09	-0.42	-0.89	0.45	-0.05	-0.67	-0.41
1	1	376	16	26	0.08	-1.40	-1.27	-1.15	-0.76	-0.67	0.81
1	1	377	7	343	0.22	0.87	-0.89	-0.65	-0.35	2.64	-1.45
1	1	378	10	39	0.06	-1.04	-1.27	-1.11	-0.76	-0.67	0.91
1	1	379	8	229	0.09	0.17	0.68	-0.63	-0.05	-0.67	-0.85
1	1	380	15	175	0.14	0.41	-0.89	-0.46	-0.05	-0.67	0.41
1	1	381	13	35	0.07	-1.28	-1.27	-0.82	-0.76	-0.67	1.03
1	1	382	14	220	0.09	-0.81	-0.08	0.45	-0.05	-0.67	-0.96
1	1	383	11	283	0.12	0.38	2.30	-0.05	-0.05	-0.67	1.46
1	1	384	10	134	0.12	-0.09	1.02	-0.78	-0.76	-0.67	1.36
1	1	385	14	416	0.08	1.33	-0.89	2.29	2.79	0.43	-0.30
1	1	386	15	76	0.18	-1.35	-0.94	-0.63	-0.81	0.43	0.43
1	1	387	23	157	0.08	-0.37	-0.89	-0.04	-0.05	-0.67	0.67
1	1	388	4	391	0.26	1.95	0.49	0.42	-0.05	2.64	0.27
1	1	389	6	105	0.08	-0.68	0.68	-1.25	-1.11	-0.67	-1.44
1	1	390	8	315	0.16	0.60	-1.08	0.37	1.37	0.43	0.25
1	1	391	14	72	0.04	-0.78	-0.89	-0.76	-0.76	-0.67	0.96
1	1	392	8	259	0.15	0.35	0.79	-0.12	-0.05	0.43	1.28
1	1	393	18	222	0.1	-0.38	-0.08	0.45	-0.05	-0.67	-0.53
1	1	394	11	333	0.06	1.49	-0.08	0.46	1.37	-0.67	0.89
1	1	395	15	276	0.1	-0.28	0.68	0.32	-0.05	0.43	-0.74
1	1	396	9	16	0.11	-1.53	-1.27	-1.19	-1.03	-0.67	0.53
1	1	397	9	364	0.08	0.27	-0.08	1.20	1.37	0.43	-0.75
1	1	398	10	211	0.14	0.00	-1.00	-0.05	-0.05	0.43	0.18
1	1	399	7	384	0.25	0.30	0.74	1.76	1.37	0.43	-0.80
1	1	400	6	177	0.14	1.15	-0.08	-0.61	-0.76	-0.67	0.67
1	1	401	19	203	0.11	-0.13	-1.01	0.05	-0.05	0.43	0.76
1	1	402	8	312	0.09	0.73	-0.08	0.45	1.37	-0.67	1.41
1	1	403	9	258	0.2	-0.18	-0.89	2.29	-0.05	-0.67	-0.38
1	1	404	11	244	0.16	-1.09	0.68	0.39	-0.76	0.43	-0.94
1	1	405	11	319	0.21	0.03	0.54	2.29	-0.05	-0.67	-0.36
1	1	406	15	159	0.08	-0.10	-1.27	0.08	-0.05	-0.67	0.91
1	1	407	33	221	0.14	0.64	-0.08	-0.05	-0.05	-0.67	0.68
1	1	408	11	112	0.09	-0.50	0.68	-1.18	-1.08	-0.67	0.48
1	1	409	17	399	0.31	1.42	0.73	0.26	1.37	2.64	0.75
1	1	410	5	20	0.08	-1.28	-1.27	-1.05	-0.90	-0.67	-1.12
1	1	411	6	34	0.08	-0.85	-0.08	-1.25	-1.11	-0.67	-1.44
1	1	412	6	426	0.2	3.36	0.68	-0.27	1.37	2.64	-0.22
1	1	413	12	285	0.1	0.72	-1.24	0.30	1.37	-0.67	0.32
1	1	414	8	363	0.08	1.28	1.11	0.45	1.37	-0.67	1.36
1	1	415	8	437	0.27	1.02	1.00	3.07	2.79	2.64	-2.30
1	1	416	7	284	0.19	0.20	-0.66	-0.74	-0.05	2.64	-0.71
1	1	417	9	264	0.06	0.29	-0.89	0.45	1.37	-0.67	0.66
1	1	418	21	238	0.21	0.24	-0.08	-0.09	-0.05	0.43	0.69
1	1	419	3	296	0.02	-0.47	0.68	0.03	-0.05	0.43	-2.34
1	1	420	10	100	0.16	-0.44	-0.97	-0.87	-0.76	0.43	1.21
1	1	421	11	174	0.15	-0.61	0.68	-0.02	-0.76	-0.67	0.17
1	1	422	9	311	0.25	1.52	-0.53	-0.55	-0.76	2.64	0.36
1	1	423	6	135	0.09	-0.58	-1.27	0.12	-0.05	-0.67	1.29
1	1	424	5	368	0.15	2.37	0.68	0.36	1.37	-0.67	0.68
1	1	425	9	373	0.12	1.65	0.68	0.39	1.37	0.43	-0.01
1	1	426	10	228	0.13	0.42	-0.08	-0.33	-0.05	-0.67	-0.92
1	1	427	20	86	0.22	-1.23	-0.93	-0.85	-0.83	0.43	-0.46
1	1	428	7	106	0.06	-0.07	-0.89	-0.76	-0.76	-0.67	0.98
1	1	429	8	138	0.12	-0.45	-0.99	-0.78	-0.76	0.43	-0.77
1	1	430	8	30	0.12	-0.51	1.05	-1.19	-1.11	-0.67	1.36
1	1	431	19	357	0.16	1.03	0.68	-0.27	-0.05	2.64	0.61
1	1	432	20	406	0.38	0.02	2.30	0.99	-0.05	2.64	-1.12
1	1	433	10	302	0.26	1.83	0.53	-0.09	-0.33	0.43	0.24
1	1	434	5	73	0.04	-0.72	-0.89	-0.95	-0.76	-0.67	0.82
1	1	435	1	440	0	5.51	0.68	3.07	4.20	2.64	0.50
1	1	436	14	300	0.29	2.04	0.68	0.38	-0.15	-0.67	0.69
1	1	437	19	74	0.1	-1.10	-0.89	-0.81	-0.76	-0.67	-0.90
1	1	438	7	347	0.13	1.21	-0.08	0.41	1.37	0.43	0.59
1	1	439	31	374	0.28	-0.27	0.87	0.49	-0.05	2.64	-1.24
1	1	440	17	233	0.2	-0.48	0.68	-0.10	-0.13	0.43	0.60

Now let us understand what each column in the above table means:

Segment.Level - Level of the cell. In this case, we have performed Vector Quantization for depth 1. Hence Segment Level is 1.
Segment.Parent - Parent segment of the cell.
Segment.Child (Cell.Number) - The children of a particular cell. In this case, it is the total number of cells at which we achieved the defined compression percentage.
n - No of points in each cell.
Cell.ID - Cell_ID’s are generated for the multivariate data using 1-D Sammon’s Projection algorithm.
Quant.Error - Quantization Error for each cell.

All the columns after this will contain centroids for each cell. They can also be called a codebook, which represents a collection of all centroids or codewords.

Now, let’s check the compression summary for HVT (map A). The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.

mapA_compression_summary <- map_A[[3]]$compression_summary %>%  dplyr::mutate_if(is.numeric, funs(round(.,4)))
compressionSummaryTable(mapA_compression_summary)

segmentLevel	noOfCells	noOfCellsBelowQuantizationError	percentOfCellsBelowQuantizationErrorThreshold	parameters
1	440	355	0.81	n_cells: 440 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans

As it can be seen from the table above, 81% of the cells have hit the quantization threshold error.Since we are successfully able to attain the desired compression percentage, so we will not further subdivide the cells

Now let’s try to understand plotHVT function. The parameters have been explained in detail below:

plotHVT(hvt.results, line.width, color.vec, pch1 = 21, centroid.size = 3, title = NULL, maxDepth = 1)

hvt.results - A list containing the output of the HVT function which has the details of the tessellations to be plotted.
line.width - A vector indicating the line widths of the tessellation boundaries for each layer.
color.vec - A vector indicating the colors of the tessellations boundaries at each layer.
pch1 - Symbol type of the centroids of the tessellations (parent levels). Refer points (default = 21)
centroid.size - Size of centroids of first level tessellations (default = 3).
title - Set a title for the plot (default = NULL).

Let’s plot the Voronoi tessellation for layer 1 (map A).

muHVT::plotHVT(map_A,
        line.width = c(0.4), 
        color.vec = c("#141B41"),
        centroid.size = 0.01,
        maxDepth = 1)

Figure 2: The Voronoi Tessellation for layer 1 (map A) shown for the 440 cells in the dataset ’computers’

Heat Maps

We will now overlay all the features as heatmap over the Voronoi Tessellation plot for better visualization and identification of patterns, trends, and variations in the data.

Let’s have a look at the function hvtHmap that we will use to overlay features as heatmap.

hvtHmap(hvt.results, dataset, child.level, hmap.cols, color.vec ,line.width, palette.color = 6)

hvt.results - A list of results obtained from the HVT function (map_A).
dataset - A dataframe containing the variables to overlay as a heatmap. The user can pass an external dataset or the dataset that was used to perform hierarchical vector quantization. The dataset should have the same number of points as the dataset used to perform hierarchical Vector Quantization in the HVT function.
child.level - A number indicating the level for which the heat map is to be plotted.
hmap.cols - The column number of column name from the dataset indicating the variables for which the heat map is to be plotted. To plot the quantization error as heatmap, pass 'quant_error'. Similarly to plot the no of points in each cell as heatmap, pass 'no_of_points' as a parameter.
color.vec - A color vector such that length(color.vec) = child.level (default = NULL).
line.width - A line width vector such that length(line.width) = child.level (default = NULL).
palette.color - A number indicating the heat map color palette. 1 - rainbow, 2 - heat.colors, 3 - terrain.colors, 4 - topo.colors, 5 - cm.colors, 6 - BlCyGrYlRd (Blue,Cyan,Green,Yellow,Red) color (default = 6).
show.points - A boolean indicating whether the centroids should be plotted on the tessellations (default = FALSE).

Now let’s plot the Voronoi Tessellation with the heatmap overlaid for all the features in the torus data for better visualization and interpretation of data patterns and distributions.

The heatmaps displayed below provides a visual representation of the spatial characteristics of the computers data, allowing us to observe patterns and trends in the distribution of each of the features (n,price,speed,hd,ram,screen,ads). The sheer green shades highlight regions with higher values in each of the heatmaps, while the indigo shades indicate areas with the lowest values in each of the heatmaps. By analyzing these heatmaps, we can gain insights into the variations and relationships between each of these features within the computers data.


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "n",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 3: The Voronoi Tessellation with the heat map overlaid for No. of entities in each cell


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "price",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 4: The Voronoi Tessellation with the heat map overlaid for variable ’price’ in the ’computers’ dataset


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "speed",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 5: The Voronoi Tessellation with the heat map overlaid for variable ’speed’ in the ’computers’ dataset


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "hd",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 6: The Voronoi Tessellation with the heat map overlaid for variable ’hd’ in the ’computers’ dataset


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "ram",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 7: The Voronoi Tessellation with the heat map overlaid for variable ’ram’ in the ’computers’ dataset


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "screen",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 8: The Voronoi Tessellation with the heat map overlaid for variable ’screen’ in the ’computers’ dataset


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "ads",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 9: The Voronoi Tessellation with the heat map overlaid for variable ’ads’ in the ’computers’ dataset

4 Map B : Compressed Novelty Map

Let us try to visualize the Map B from the flow diagram below.

Figure 10: Flow map with highlighted bounding box in red around map B

In this section, we will manually figure out the novelty cells from the plotted map A and store it in identified_Novelty_cells variable.

Note: For manual selecting the novelty cells from map A, one can enhance its interactivity by adding plotly elements to the code. This will transform map A into an interactive plot, allowing users to actively engage with the data. By hovering over the centroids of the cells, a tag containing segment child information will be displayed. Users can explore the map by hovering over different cells and selectively choose the novelty cells they wish to consider. Added an image for reference.

Figure 11: Manually selecting novelty cells

The removeNovelty function removes the identified novelty cell(s) from the dataset and stores those records separately.

It takes input as the cell number (Segment.Child) of the manually identified novelty cell(s) from the above table and the compressed HVT map (map A). It returns a list of two items: dataset with novelty records, and a subset of the dataset without the novelty records.

identified_Novelty_cells <<- c(73,321,332,338,435)
output_list <- removeNovelty(identified_Novelty_cells, map_A)

[1] “The following cell(s) have been removed as outliers from the dataset: 73 321 332 338 435”

data_with_novelty <- output_list[[1]]
dataset_without_novelty <- output_list[[2]]

Let’s have a look at the data with novelties.For the sake of brevity, we will only show the first 10 rows.

novelty_data <- data_with_novelty
novelty_data$Row.No <- row.names(novelty_data)
novelty_data <- novelty_data %>% dplyr::select("Row.No","Cell.ID","Cell.Number","price","speed","hd","ram","screen","ads")
colnames(novelty_data) <- c("Row.No","Cell.ID","Segment.Child","price","speed","hd","ram","screen","ads")
novelty_data %>% head(100) %>% 
  as.data.frame() %>%
  Table(scroll = T, limit = 20)

Row.No	Cell.ID	Segment.Child	price	speed	hd	ram	screen	ads
1	429	73	3.0762240	0.6794579	0.0421969	4.2031619	0.4307274	0.7120258
2	438	321	2.6449847	0.6794579	6.5710368	1.3676416	0.4307274	0.6851382
3	438	321	1.6578103	2.2969425	6.5710368	1.3676416	0.4307274	-1.2507722
4	439	332	0.9130997	0.6794579	4.6232922	2.7854017	0.4307274	-2.4607161
5	439	332	1.8569770	1.1076156	4.6232922	4.2031619	2.6404120	-2.4607161
6	439	332	1.5192595	2.2969425	4.6232922	4.2031619	0.4307274	-2.4607161
7	439	332	1.4482522	1.1076156	4.6232922	4.2031619	0.4307274	-2.4607161
8	439	332	1.8569770	1.1076156	4.6232922	4.2031619	2.6404120	-2.4607161
9	439	332	1.1555636	2.2969425	4.6232922	2.7854017	0.4307274	-2.4607161
10	439	332	1.7773103	1.1076156	4.6232922	4.2031619	0.4307274	-2.4607161
11	439	332	1.3460710	0.6794579	4.6232922	4.2031619	0.4307274	-2.4607161
12	439	332	1.4482522	1.1076156	4.6232922	4.2031619	0.4307274	-2.4607161
13	218	338	-1.4959522	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.2993903
14	218	338	-1.5115391	0.6794579	0.4473277	-0.7589986	-0.6741149	-1.9767386
15	218	338	-1.1668940	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.2859465
16	218	338	-1.4959522	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.4472723
17	218	338	-1.5981334	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.2859465
18	218	338	-1.1668940	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.2993903
19	218	338	-1.1668940	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.4472723
20	218	338	-1.3227637	0.6794579	0.4473277	-0.7589986	-0.6741149	-1.9767386

4.1 Voronoi Tessellation with highlighted novelty cell

The plotCells function is used to plot the Voronoi tessellation using the compressed HVT map (map A) and highlights the identified novelty cell(s) in red on the map.

plotCells(identified_Novelty_cells, map_A,line.width = c(0.4),centroid.size = 0.01)

Figure 12: The Voronoi Tessellation constructed using the compressed HVT map (map A) with the novelty cell(s) highlighted in red

We pass the dataframe with novelty records to HVT function along with other model parameters mentioned below to generate map B (layer2)

Model Parameters

Number of Cells at each Level = 5
Maximum Depth = 1
Quantization Error Threshold = 0.2
Error Metric = Max
Distance Metric = Manhattan

colnames(data_with_novelty) <- c("Cell.ID","Segment.Child","price","speed","hd","ram","screen","ads")
dataset_with_novelty <- data_with_novelty[,-1:-2]
map_B <- list()
mapA_scale_summary = map_A[[3]]$scale_summary
map_B <- muHVT::HVT(dataset_with_novelty,
                  n_cells = 5,
                  depth = 1,
                  quant.err = 0.2,
                  projection.scale = 10,
                  normalize = F,
                  distance_metric = "L1_Norm",
                  error_metric = "max",
                  quant_method = "kmeans",
                  diagnose = F
                  )

The datatable displayed below is the summary from map B (layer 2).

summaryTable(map_B[[3]]$summary,scroll = T,limit = 500)

Segment.Level	Segment.Parent	Segment.Child	n	Cell.ID	Quant.Error	price	speed	hd	ram	screen	ads
1	1	1	2	5	0.01	-1.55	-0.08	0.45	-0.76	-0.67	-1.98
1	1	2	9	3	0.49	1.48	1.28	4.62	3.89	0.92	-2.46
1	1	3	2	2	0.38	2.15	1.49	6.57	1.37	0.43	-0.28
1	1	4	9	4	0.08	-1.33	0.68	0.45	-0.76	-0.67	-2.22
1	1	5	2	1	0.66	4.29	0.68	1.55	4.20	1.54	0.60

5 Map C : Compressed Map without Novelty

Let us try to visualize the compressed Map C from the flow diagram below.

Figure 13:Flow map with highlighted bounding box in red around compressed map C

5.0.1 Iteration 1:

With the Novelties removed, we construct another hierarchical Voronoi tessellation map C layer 2 on the dataset without Novelty and below mentioned model parameters.

Model Parameters

Number of Cells at each Level = 10
Maximum Depth = 2
Quantization Error Threshold = 0.2
Error Metric = Max
Distance Metric = Manhattan

map_C <- list()
mapA_scale_summary = map_A[[3]]$scale_summary
map_C <- muHVT::HVT(dataset_without_novelty,
                  n_cells = 10,
                  depth = 2,
                  quant.err = 0.2,
                  projection.scale = 10,
                  normalize = F,
                  distance_metric = "L1_Norm",
                  error_metric = "max",
                  quant_method = "kmeans",
                  diagnose = F,
                  scale_summary = mapA_scale_summary)

Now let’s check the compression summary for HVT (map C). The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.

mapC_compression_summary <- map_C[[3]]$compression_summary %>%  dplyr::mutate_if(is.numeric, funs(round(.,4)))
compressionSummaryTable(mapC_compression_summary)

segmentLevel	noOfCells	noOfCellsBelowQuantizationError	percentOfCellsBelowQuantizationErrorThreshold	parameters
1	10	0	0	n_cells: 10 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans
2	100	7	0.07	n_cells: 10 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans

As it can be seen from the table above, 0% of the cells have hit the quantization threshold error in level 1 and 7% of the cells have hit the quantization threshold error in level 2

5.0.2 Iteration 2:

Since, we are yet to achive atleast 80% compression at depth 2. Let’s try to compress again using the below mentioned set of model parameters.

Model Parameters

Number of Cells at each Level = 23
Maximum Depth = 2
Quantization Error Threshold = 0.2
Error Metric = Max
Distance Metric = Manhattan

map_C <- list()
map_C <- muHVT::HVT(dataset_without_novelty,
                  n_cells = 23,    #23
                  depth = 2,
                  quant.err = 0.2,
                  projection.scale = 10,
                  normalize = F,
                  distance_metric = "L1_Norm",
                  error_metric = "max",
                  quant_method = "kmeans",
                  diagnose = F,
                  scale_summary = mapA_scale_summary)

The datatable displayed below is the summary from map C (layer2).

summaryTable(map_C[[3]]$summary,scroll = T,limit = 500)

Segment.Level	Segment.Parent	Segment.Child	n	Cell.ID	Quant.Error	price	speed	hd	ram	screen	ads
1	1	1	391	512	0.56	-1.27	-1.04	-0.91	-0.83	-0.59	0.86
1	1	2	105	96	1.02	2.08	0.75	0.11	0.71	2.64	0.56
1	1	3	190	55	1.37	1.48	0.02	2.32	2.63	0.12	-0.22
1	1	4	86	15	0.94	0.87	1.27	2.61	2.57	1.33	-1.76
1	1	5	257	326	0.49	0.41	0.45	-0.08	-0.20	-0.67	0.66
1	1	6	116	201	0.77	1.57	0.45	-0.24	0.27	-0.30	-1.30
1	1	7	235	228	0.58	0.85	-0.82	0.42	1.37	-0.47	0.52
1	1	8	149	185	0.84	-0.79	1.73	0.76	-0.22	-0.28	-1.75
1	1	9	353	306	0.69	-0.77	0.55	0.28	-0.22	-0.17	-0.79
1	1	10	234	297	0.54	0.25	0.50	-0.11	-0.16	0.43	0.66
1	1	11	330	478	0.56	-1.18	-0.79	-0.70	-0.76	-0.52	-0.69
1	1	12	373	434	0.6	-0.50	0.40	-0.84	-0.84	-0.56	0.66
1	1	13	209	147	0.81	1.32	0.99	0.36	1.37	-0.01	0.70
1	1	14	337	471	0.36	-0.51	-0.97	-0.87	-0.75	-0.67	0.57
1	1	15	283	404	0.71	0.06	-0.40	-0.78	-0.40	-0.42	-1.42
1	1	16	168	142	0.85	0.13	0.66	0.48	-0.02	2.64	-0.97
1	1	17	285	384	0.53	-0.04	-0.96	0.05	-0.12	-0.65	0.54
1	1	18	118	301	0.75	0.35	2.30	-0.13	-0.29	-0.28	0.82
1	1	19	256	414	0.47	-0.47	-0.84	-0.43	-0.48	0.43	0.60
1	1	20	78	57	1	0.18	1.67	2.05	1.31	0.39	-1.97
1	1	21	171	360	0.64	0.24	-0.46	-0.37	-0.41	2.64	0.59
1	1	22	170	130	1.28	0.28	-0.01	1.64	0.99	0.21	-0.62
1	1	23	89	219	0.88	1.98	0.59	0.20	-0.20	0.12	0.58
2	1	1	19	528	0.09	-1.31	-1.17	-1.19	-1.11	-0.67	0.99
2	1	2	18	479	0.31	-1.42	-0.87	-0.21	-0.72	-0.67	0.53
2	1	3	28	521	0.1	-1.47	-1.22	-1.11	-0.76	-0.67	0.89
2	1	4	10	530	0.14	-1.84	-1.04	-1.20	-1.11	-0.67	0.79
2	1	5	27	519	0.1	-1.20	-1.09	-1.19	-1.11	-0.67	0.50
2	1	6	13	524	0.18	-1.36	-1.10	-0.99	-0.87	0.43	1.36
2	1	7	5	523	0.04	-1.19	-1.27	-0.78	-0.76	-0.67	1.57
2	1	8	23	494	0.06	-1.18	-0.89	-0.78	-0.76	-0.67	0.72
2	1	9	27	514	0.14	-1.35	-1.27	-1.08	-0.76	-0.67	0.39
2	1	10	35	510	0.1	-1.08	-1.27	-0.88	-0.76	-0.67	0.90
2	1	11	6	529	0.15	-1.22	-0.62	-1.19	-1.11	-0.67	1.50
2	1	12	10	513	0.07	-0.95	-0.89	-0.85	-0.76	-0.67	1.57
2	1	13	10	517	0.22	-1.70	-1.19	-0.73	-0.83	-0.67	0.57
2	1	14	11	472	0.17	-1.19	-1.13	-0.33	-0.05	-0.67	0.97
2	1	15	11	507	0.19	-1.00	-0.93	-1.17	-0.63	-0.67	0.97
2	1	16	7	531	0.07	-1.58	-1.27	-1.18	-0.96	-0.67	1.57
2	1	17	26	499	0.09	-1.31	-0.89	-0.92	-0.76	-0.67	0.36
2	1	18	23	515	0.07	-0.99	-0.89	-1.19	-1.11	-0.67	0.92
2	1	19	13	520	0.14	-1.56	-0.98	-0.74	-0.76	-0.67	1.34
2	1	20	15	489	0.12	-1.05	-0.89	-0.26	-0.76	-0.67	1.31
2	1	21	16	503	0.18	-1.48	-0.79	-0.73	-0.76	-0.67	0.76
2	1	22	15	518	0.26	-1.37	-1.07	-1.09	-1.07	0.43	0.66
2	1	23	23	500	0.07	-1.03	-0.89	-0.82	-0.76	-0.67	1.06
2	2	1	4	108	0.12	2.31	0.68	-0.12	-0.05	2.64	1.31
2	2	2	3	118	0.11	2.53	0.68	-0.47	-0.76	2.64	0.36
2	2	3	7	99	0.21	1.06	0.80	0.29	1.37	2.64	0.35
2	2	4	2	89	0.06	1.09	2.30	0.25	-0.05	2.64	1.57
2	2	5	7	59	0.13	3.21	0.68	-0.27	1.37	2.64	0.53
2	2	6	7	88	0.11	1.85	0.68	0.16	1.37	2.64	0.85
2	2	7	2	145	0.11	1.14	0.68	0.10	-0.05	2.64	1.36
2	2	8	3	38	0.11	3.32	0.68	-0.27	1.37	2.64	-0.78
2	2	9	6	100	0.17	2.86	0.68	0.10	-0.05	2.64	0.48
2	2	10	2	24	0.11	3.50	2.30	1.23	-0.05	2.64	-0.23
2	2	11	2	12	0.38	4.64	0.68	1.36	0.66	2.64	0.37
2	2	12	6	56	0.25	1.52	2.30	0.32	1.37	2.64	0.77
2	2	13	4	50	0.34	1.64	0.49	1.90	1.37	2.64	0.14
2	2	14	13	76	0.22	2.42	0.68	0.03	1.37	2.64	0.53
2	2	15	4	103	0.12	1.08	2.30	0.14	-0.05	2.64	0.52
2	2	16	11	167	0.13	1.23	0.68	-0.20	-0.05	2.64	0.59
2	2	17	1	107	0	1.99	-0.08	0.49	-0.05	2.64	-0.62
2	2	18	4	84	0.15	1.52	0.68	0.34	1.37	2.64	1.36
2	2	19	5	169	0.17	1.81	-0.08	-0.29	-0.19	2.64	0.99
2	2	20	4	70	0.11	2.69	-0.89	-0.27	1.37	2.64	0.69
2	2	21	4	122	0.11	1.99	0.68	0.22	-0.05	2.64	0.57
2	2	22	3	60	0.12	2.74	-0.89	-0.27	1.37	2.64	-0.78
2	2	23	1	195	0	1.63	0.68	-0.95	-0.76	2.64	0.69
2	3	1	5	39	0.04	1.54	-0.08	2.29	2.79	0.43	-0.81
2	3	2	3	9	0.05	1.71	-0.89	2.29	2.79	2.64	-0.55
2	3	3	10	51	0.07	1.10	-0.08	2.29	2.79	0.43	-0.56
2	3	4	5	32	0.39	3.74	-0.10	2.60	1.37	-0.67	0.96
2	3	5	3	19	0.02	2.16	2.30	2.29	2.79	-0.67	0.04
2	3	6	1	20	0	2.03	2.30	2.29	2.79	0.43	0.35
2	3	7	16	49	0.18	1.49	-0.89	2.29	2.79	0.43	-0.06
2	3	8	7	40	0.09	1.80	-0.08	2.29	2.79	0.43	-0.26
2	3	9	2	54	0.05	2.90	0.68	3.73	-0.05	-0.67	0.69
2	3	10	1	91	0	1.16	0.68	2.29	1.37	-0.67	0.04
2	3	11	7	46	0.05	1.16	0.68	2.29	2.79	0.43	-0.49
2	3	12	4	77	0.12	1.66	0.68	2.44	1.37	0.43	0.80
2	3	13	7	37	0.08	1.63	0.68	2.29	2.79	0.43	-0.73
2	3	14	12	53	0.1	1.69	0.68	2.29	2.79	-0.67	0.38
2	3	15	2	43	0.04	3.07	0.68	2.29	1.37	-0.67	-0.87
2	3	16	5	23	0.04	2.06	2.30	2.29	2.79	-0.67	0.35
2	3	17	12	45	0.06	0.92	0.68	2.29	2.79	0.43	-0.86
2	3	18	5	82	0.13	1.35	0.68	2.47	1.37	0.43	0.11
2	3	19	5	17	0.04	2.23	2.30	2.29	2.79	-0.67	0.69
2	3	20	24	61	0.12	1.30	-0.89	2.29	2.79	-0.67	0.33
2	3	21	9	36	0.18	2.07	0.68	2.29	2.79	0.43	-0.03
2	3	22	13	52	0.11	0.65	-0.08	2.29	2.79	0.43	-1.03
2	3	23	32	48	0.13	1.15	-0.89	2.29	2.79	0.43	-0.66
2	4	1	5	7	0.11	0.51	2.30	2.05	1.37	2.64	-2.33
2	4	2	8	18	0.07	0.52	0.68	3.07	2.79	0.43	-2.36
2	4	3	6	41	0.05	0.79	0.68	2.29	2.79	0.43	-1.24
2	4	4	2	3	0	1.08	2.30	3.07	2.79	0.43	-2.46
2	4	5	3	8	0.13	1.91	0.43	2.29	2.79	2.64	-0.55
2	4	6	1	11	0	0.01	1.11	3.07	2.79	0.43	-2.45
2	4	7	3	26	0.03	0.73	-0.08	3.07	2.79	0.43	-1.98
2	4	8	1	4	0	0.99	-0.08	3.07	2.79	2.64	-1.98
2	4	9	3	2	0.05	1.48	2.30	2.29	2.79	2.64	-1.08
2	4	10	2	14	0.02	0.81	0.68	3.07	2.79	0.43	-2.46
2	4	11	1	6	0	0.74	2.30	3.07	2.79	0.43	-1.98
2	4	12	2	16	0.02	0.89	1.11	3.07	2.79	0.43	-1.98
2	4	13	2	29	0.04	0.84	2.30	1.70	1.37	2.64	-1.02
2	4	14	6	5	0.03	0.74	2.30	3.07	2.79	0.43	-2.40
2	4	15	7	1	0.23	1.03	1.16	3.07	2.79	2.64	-2.34
2	4	16	5	25	0.02	1.02	2.30	2.29	2.79	0.43	-1.23
2	4	17	6	22	0.25	0.85	-0.22	2.29	2.79	2.64	-0.97
2	4	18	2	13	0.05	1.21	0.68	2.29	2.79	2.64	-1.10
2	4	19	5	30	0.11	0.32	0.68	2.17	1.37	2.64	-2.29
2	4	20	6	10	0.04	0.69	1.11	3.07	2.79	0.43	-2.32
2	4	21	1	34	0	0.30	-0.08	2.29	1.37	2.64	-1.98
2	4	22	6	21	0.06	1.46	2.30	2.29	2.79	0.43	-1.01
2	4	23	3	28	0.01	1.00	2.30	2.29	2.79	0.43	-0.79
2	5	1	24	300	0.13	0.57	0.68	0.13	-0.05	-0.67	0.72
2	5	2	9	332	0.08	-0.44	0.68	-0.04	-0.05	-0.67	0.21
2	5	3	5	310	0.12	0.37	-0.08	0.29	-0.05	-0.67	-0.14
2	5	4	21	312	0.17	0.35	0.86	0.11	-0.05	-0.67	1.39
2	5	5	6	311	0.06	-0.28	0.68	0.45	-0.05	-0.67	0.30
2	5	6	8	287	0.15	0.45	0.68	0.16	-0.05	-0.67	-0.20
2	5	7	5	305	0.06	-0.07	0.68	0.45	-0.05	-0.67	0.48
2	5	8	7	333	0.12	0.31	0.68	-0.57	-0.05	-0.67	0.57
2	5	9	12	319	0.06	-0.12	0.68	0.04	-0.05	-0.67	0.14
2	5	10	13	358	0.15	-0.04	-0.08	0.00	-0.05	-0.67	0.83
2	5	11	11	371	0.16	0.41	0.68	-0.40	-0.76	-0.67	0.94
2	5	12	7	314	0.16	1.02	0.68	-0.39	-0.15	-0.67	1.12
2	5	13	17	363	0.2	0.66	0.63	-0.69	-0.76	-0.67	0.15
2	5	14	15	278	0.11	1.06	0.68	0.06	-0.05	-0.67	0.76
2	5	15	8	356	0.16	0.13	0.79	-0.13	-0.76	-0.67	0.38
2	5	16	11	275	0.19	1.06	0.68	-0.09	-0.05	-0.67	0.19
2	5	17	23	334	0.13	0.51	-0.08	0.01	-0.05	-0.67	0.91
2	5	18	17	324	0.11	0.69	-0.08	-0.08	-0.05	-0.67	0.46
2	5	19	12	402	0.15	0.15	-0.08	-0.20	-0.76	-0.67	0.94
2	5	20	7	386	0.14	1.08	-0.08	-0.62	-0.76	-0.67	0.63
2	5	21	12	336	0.2	-0.17	0.68	-0.04	-0.05	-0.67	0.86
2	5	22	7	364	0.13	0.24	-0.08	-0.05	-0.05	-0.67	1.57
2	5	23	0	NA	NA	NA	NA	NA	NA	NA	NA
2	6	1	6	184	0.1	2.46	0.68	0.21	-0.05	-0.67	-0.87
2	6	2	9	133	0.29	1.43	0.51	-0.44	1.37	0.43	-1.15
2	6	3	2	233	0.08	1.08	0.68	0.47	-0.05	-0.67	-0.71
2	6	4	9	247	0.22	0.90	0.59	-0.69	-0.13	0.43	-1.39
2	6	5	2	194	0.08	2.30	0.30	-0.32	-0.05	-0.67	-1.71
2	6	6	8	238	0.26	1.04	0.58	-0.29	-0.14	0.43	-0.77
2	6	7	9	127	0.06	2.98	0.68	0.28	-0.05	-0.67	-1.67
2	6	8	4	165	0.11	1.26	-0.08	-0.07	1.37	-0.67	-1.37
2	6	9	5	124	0.19	1.76	0.53	-0.29	1.37	-0.67	-1.66
2	6	10	1	101	0	3.77	0.68	0.15	-0.05	-0.67	-1.72
2	6	11	5	292	0.22	1.44	0.53	-0.77	-0.33	-0.67	-1.67
2	6	12	1	365	0	1.35	-0.08	-0.67	-0.76	-0.67	-0.62
2	6	13	4	149	0.06	1.43	0.68	0.15	1.37	-0.67	-0.87
2	6	14	10	197	0.2	1.57	0.68	-0.47	-0.33	0.43	-1.64
2	6	15	3	159	0.2	1.48	2.30	0.28	-0.05	-0.67	-0.87
2	6	16	3	280	0.1	0.85	-0.08	0.22	-0.05	-0.67	-0.68
2	6	17	9	191	0.15	1.09	-0.93	-0.29	1.37	-0.67	-1.62
2	6	18	4	140	0.08	3.03	-0.08	0.15	-0.05	-0.67	-1.65
2	6	19	7	294	0.05	0.73	0.68	-0.69	-0.05	-0.67	-1.60
2	6	20	3	112	0.09	2.96	0.68	0.12	-0.05	0.43	-1.72
2	6	21	2	270	0.04	1.61	0.68	-0.71	-0.05	-0.67	-0.87
2	6	22	8	279	0.09	0.84	0.68	-0.36	-0.05	-0.67	-0.84
2	6	23	2	237	0.08	2.30	-0.49	0.15	-0.05	-0.67	-1.12
2	7	1	8	210	0.16	0.51	-1.18	0.46	1.37	0.43	1.10
2	7	2	16	231	0.12	1.01	-0.91	0.35	1.37	-0.67	0.40
2	7	3	12	211	0.1	1.39	-0.89	0.37	1.37	-0.67	0.29
2	7	4	8	209	0.06	0.35	-0.89	1.20	1.37	-0.67	0.35
2	7	5	5	263	0.05	0.58	-0.89	0.45	1.37	-0.67	1.57
2	7	6	12	202	0.18	0.49	-1.02	0.55	1.37	0.43	0.36
2	7	7	14	264	0.08	0.43	-0.89	0.45	1.37	-0.67	0.64
2	7	8	2	166	0.09	0.99	-0.08	0.45	1.37	0.43	0.26
2	7	9	11	216	0.14	0.82	-1.03	0.15	1.37	-0.67	-0.85
2	7	10	20	220	0.09	1.20	-0.91	0.45	1.37	-0.67	0.84
2	7	11	9	265	0.07	0.48	-1.27	0.45	1.37	-0.67	0.66
2	7	12	14	190	0.21	1.09	-0.89	0.28	1.37	0.43	0.63
2	7	13	9	222	0.09	0.82	-1.06	0.45	1.37	-0.67	-0.07
2	7	14	7	168	0.2	0.97	-1.00	0.03	1.37	0.43	-0.83
2	7	15	9	204	0.2	0.72	-0.08	0.45	1.37	-0.55	1.43
2	7	16	17	243	0.09	0.82	-0.98	0.46	1.37	-0.67	0.95
2	7	17	7	262	0.04	0.22	-0.89	0.45	1.37	-0.67	0.04
2	7	18	7	285	0.07	0.28	-1.22	0.45	1.37	-0.67	1.12
2	7	19	6	179	0.12	1.31	-0.08	0.35	1.37	-0.67	-0.26
2	7	20	17	189	0.14	1.26	-0.08	0.46	1.37	-0.67	0.84
2	7	21	3	316	0.02	0.10	-1.27	0.45	1.37	-0.67	1.57
2	7	22	11	183	0.11	1.44	-0.08	0.34	1.37	-0.67	0.42
2	7	23	11	251	0.09	0.72	-1.24	0.29	1.37	-0.67	0.36
2	8	1	5	116	0.08	-0.74	0.68	1.70	-0.05	0.43	-2.35
2	8	2	2	282	0	-1.43	0.68	0.45	-0.76	0.43	-2.29
2	8	3	8	182	0.09	-0.47	2.30	0.49	-0.05	0.43	-0.85
2	8	4	10	236	0.19	-0.61	2.30	0.49	-0.05	-0.67	-1.01
2	8	5	13	87	0.15	-0.66	2.30	1.70	-0.05	-0.16	-2.28
2	8	6	4	117	0.13	-1.29	0.49	2.29	-0.76	-0.67	-2.17
2	8	7	2	64	0.02	-1.12	2.30	2.29	-0.76	-0.67	-2.37
2	8	8	3	104	0.06	-0.17	2.30	2.29	-0.05	-0.67	-0.98
2	8	9	4	170	0.08	-0.90	1.11	1.35	-0.05	0.43	-1.21
2	8	10	7	146	0.07	-0.91	2.30	0.47	-0.05	-0.67	-2.34
2	8	11	12	409	0.11	-0.78	2.30	-0.33	-0.76	-0.67	-1.03
2	8	12	7	241	0.1	-0.19	2.30	0.33	-0.05	-0.67	-0.89
2	8	13	4	258	0.09	0.04	2.30	-0.08	-0.05	-0.67	-1.04
2	8	14	9	330	0.09	-1.17	2.30	0.45	-0.76	-0.67	-2.26
2	8	15	6	212	0.13	-0.74	0.68	0.30	-0.05	0.43	-2.40
2	8	16	7	175	0.07	-0.53	2.30	0.49	-0.05	0.43	-1.23
2	8	17	4	109	0.04	-0.38	2.30	1.70	-0.05	0.43	-1.05
2	8	18	10	144	0.09	-0.89	0.68	1.70	-0.05	-0.67	-2.20
2	8	19	1	283	0	-1.59	0.68	-0.19	-0.05	0.43	-2.29
2	8	20	7	123	0.17	-0.94	2.30	0.46	-0.35	0.43	-2.27
2	8	21	3	329	0.06	-0.86	2.30	-0.29	-0.76	0.43	-0.98
2	8	22	21	277	0.11	-1.16	0.68	0.48	-0.05	-0.67	-2.25
2	8	23	0	NA	NA	NA	NA	NA	NA	NA	NA
2	9	1	33	331	0.22	-0.57	-0.08	0.41	-0.05	-0.67	-0.71
2	9	2	10	368	0.19	-0.75	0.68	-0.60	-0.76	0.43	-0.42
2	9	3	13	423	0.19	-1.02	0.68	-0.69	-0.70	-0.67	-0.83
2	9	4	8	335	0.2	-0.08	0.68	-0.49	-0.23	-0.67	-0.86
2	9	5	15	377	0.21	-1.31	-0.08	0.30	-0.76	0.43	-0.98
2	9	6	8	224	0.04	-0.74	1.11	0.50	-0.05	0.43	-1.22
2	9	7	22	261	0.13	-0.22	0.68	0.29	-0.05	0.43	-0.60
2	9	8	14	381	0.28	-0.89	0.57	-0.09	-0.51	-0.67	-0.23
2	9	9	14	257	0.08	-0.64	0.68	0.49	-0.05	0.43	-0.76
2	9	10	13	354	0.27	-1.37	0.33	0.47	-0.27	-0.67	-0.93
2	9	11	9	248	0.16	-1.00	0.68	0.57	-0.05	0.43	-1.22
2	9	12	23	323	0.13	-0.51	0.68	0.02	-0.05	-0.67	-0.46
2	9	13	10	344	0.25	-1.39	0.68	-0.30	-0.48	0.43	-1.23
2	9	14	11	267	0.11	-1.03	0.68	0.41	-0.05	0.43	-0.85
2	9	15	20	422	0.17	-1.43	0.68	-0.23	-0.72	-0.67	-1.02
2	9	16	23	206	0.26	-0.52	0.70	1.25	-0.05	0.43	-0.79
2	9	17	8	304	0.13	-0.86	0.68	-0.12	-0.05	0.43	-0.39
2	9	18	8	240	0.05	-0.72	1.11	0.50	-0.05	0.43	-0.83
2	9	19	32	303	0.35	-0.82	0.68	0.33	0.04	-0.67	-1.07
2	9	20	13	317	0.16	-1.11	0.68	0.34	-0.76	0.43	-0.90
2	9	21	10	308	0.22	-0.75	-0.24	0.33	-0.05	0.43	-0.69
2	9	22	33	296	0.2	-0.39	0.68	0.55	-0.05	-0.67	-0.56
2	9	23	3	196	0.03	-0.83	-0.08	1.70	-0.05	-0.67	-1.98
2	10	1	4	269	0.07	0.51	0.68	-0.55	-0.05	0.43	-0.62
2	10	2	8	327	0.1	-0.43	0.68	-0.12	-0.05	0.43	1.46
2	10	3	23	318	0.15	0.23	-0.08	-0.23	-0.05	0.43	0.69
2	10	4	3	346	0.12	-0.04	0.68	-0.74	-0.76	0.43	-0.31
2	10	5	9	271	0.17	0.14	0.73	0.34	-0.05	0.43	1.57
2	10	6	10	273	0.12	0.92	0.68	-0.54	-0.05	0.43	0.74
2	10	7	9	299	0.11	0.93	-0.08	-0.36	-0.05	0.43	0.66
2	10	8	6	307	0.08	-0.01	0.68	-0.52	-0.05	0.43	0.38
2	10	9	8	289	0.15	0.67	-0.08	-0.27	-0.05	0.43	-0.15
2	10	10	13	325	0.26	0.16	-0.08	0.00	-0.16	0.43	1.23
2	10	11	13	315	0.18	-0.46	0.68	-0.14	-0.05	0.43	0.64
2	10	12	10	259	0.12	0.83	0.68	-0.14	-0.05	0.43	0.32
2	10	13	7	234	0.06	-0.20	0.68	1.23	-0.05	0.43	0.35
2	10	14	12	260	0.16	0.74	0.72	0.08	-0.05	0.43	0.89
2	10	15	8	249	0.12	0.66	0.68	0.48	-0.14	0.43	0.81
2	10	16	13	268	0.09	0.04	0.68	0.49	-0.05	0.43	0.60
2	10	17	11	256	0.11	0.31	0.68	0.24	-0.05	0.43	-0.06
2	10	18	6	342	0.17	0.82	0.30	-0.76	-0.76	0.43	0.64
2	10	19	8	302	0.15	0.22	0.68	-0.33	-0.05	0.43	1.20
2	10	20	12	370	0.24	-0.28	0.75	-0.51	-0.76	0.43	0.84
2	10	21	19	281	0.11	0.29	0.68	-0.07	-0.05	0.43	0.64
2	10	22	9	290	0.13	-0.34	0.68	0.15	-0.05	0.43	0.20
2	10	23	13	351	0.11	0.21	0.68	-0.70	-0.76	0.43	0.66
2	11	1	10	411	0.22	-1.09	-0.93	0.23	-0.12	-0.67	-0.72
2	11	2	14	502	0.14	-1.13	-0.97	-1.01	-0.86	-0.67	-0.09
2	11	3	7	481	0.2	-0.81	-0.08	-1.20	-1.11	-0.36	-0.96
2	11	4	19	516	0.16	-0.95	-0.95	-1.13	-0.91	-0.67	-1.64
2	11	5	14	482	0.12	-1.58	-0.89	-0.16	-0.76	-0.67	-0.68
2	11	6	24	498	0.16	-1.36	-0.94	-0.85	-0.77	-0.67	-0.46
2	11	7	17	527	0.25	-1.29	-1.25	-1.06	-0.84	-0.61	-1.65
2	11	8	23	484	0.15	-1.01	-0.92	-0.84	-0.79	-0.67	-0.72
2	11	9	18	509	0.1	-1.67	-0.91	-0.78	-0.76	-0.67	-0.75
2	11	10	10	435	0.15	-0.94	-0.08	-0.59	-0.69	-0.67	-0.89
2	11	11	19	491	0.25	-1.54	-0.93	-0.50	-0.74	-0.67	-0.11
2	11	12	12	452	0.25	-1.47	-0.76	-0.12	-0.79	0.43	-0.55
2	11	13	8	445	0.17	-1.22	-0.08	-0.43	-0.67	-0.67	-0.19
2	11	14	8	427	0.08	-0.60	-0.89	-0.75	-0.05	-0.67	-0.70
2	11	15	17	466	0.16	-0.52	-0.98	-0.84	-0.76	-0.67	-0.67
2	11	16	17	511	0.16	-1.09	-1.00	-1.06	-0.88	-0.67	-1.12
2	11	17	8	437	0.08	-0.69	-0.08	-0.75	-0.76	-0.67	-0.48
2	11	18	9	465	0.09	-1.33	-0.08	-0.78	-0.76	-0.67	-0.56
2	11	19	10	525	0.18	-1.69	-1.19	-1.13	-0.90	-0.67	-0.23
2	11	20	30	473	0.29	-1.06	-0.87	-0.89	-0.79	0.43	-0.61
2	11	21	6	450	0.1	-1.62	-0.08	-0.06	-0.76	-0.67	-0.70
2	11	22	18	457	0.26	-1.05	-0.89	-0.10	-0.68	-0.67	-0.16
2	11	23	12	453	0.09	-1.43	-0.08	-0.29	-0.76	-0.67	-1.02
2	12	1	25	397	0.2	-0.09	0.68	-0.76	-0.70	-0.67	0.48
2	12	2	13	394	0.18	-0.08	0.68	-0.81	-0.79	-0.67	-0.16
2	12	3	13	413	0.17	0.17	0.68	-1.08	-0.87	-0.67	0.72
2	12	4	21	474	0.12	-0.56	0.68	-1.19	-1.10	-0.67	0.96
2	12	5	15	442	0.11	-0.32	0.68	-1.19	-1.09	-0.67	0.31
2	12	6	12	458	0.26	-0.88	0.68	-0.73	-0.70	-0.67	1.24
2	12	7	17	405	0.25	-0.57	0.68	-0.86	-0.90	0.43	0.44
2	12	8	23	421	0.14	0.08	-0.08	-0.77	-0.76	-0.67	0.62
2	12	9	24	468	0.16	-0.66	-0.08	-1.13	-0.98	-0.67	0.37
2	12	10	6	433	0.13	-0.26	-0.08	-0.91	-0.88	-0.67	-0.11
2	12	11	8	449	0.09	-0.21	0.84	-0.78	-0.76	-0.67	1.57
2	12	12	10	454	0.22	-0.75	0.72	-0.93	-0.94	0.43	1.25
2	12	13	19	428	0.17	-0.83	-0.08	-0.26	-0.68	-0.67	0.55
2	12	14	8	506	0.12	-0.51	1.05	-1.19	-1.11	-0.67	1.36
2	12	15	9	443	0.16	-0.77	-0.08	-0.08	-0.76	-0.67	1.22
2	12	16	21	441	0.19	-0.46	-0.08	-0.80	-0.62	-0.67	0.85
2	12	17	21	436	0.14	-0.85	0.68	-0.87	-0.76	-0.67	0.44
2	12	18	27	492	0.15	-0.84	-0.08	-1.17	-1.05	-0.67	0.90
2	12	19	9	495	0.15	-0.81	-0.08	-0.91	-0.88	-0.67	1.48
2	12	20	19	431	0.24	-0.99	0.68	-0.80	-0.78	-0.67	-0.34
2	12	21	16	387	0.2	-0.62	0.68	-0.11	-0.67	-0.67	0.49
2	12	22	10	477	0.19	-0.79	-0.08	-1.19	-1.11	0.43	0.74
2	12	23	27	415	0.13	-0.11	0.76	-0.76	-0.76	-0.67	1.01
2	13	1	10	138	0.21	0.94	0.68	0.67	1.37	0.43	-0.06
2	13	2	14	131	0.21	2.10	0.68	0.25	1.37	0.43	0.77
2	13	3	4	198	0.02	0.61	0.68	0.45	1.37	-0.67	0.69
2	13	4	2	47	0.19	1.96	1.49	2.29	1.37	0.43	1.36
2	13	5	8	143	0.08	1.28	1.11	0.45	1.37	-0.67	1.36
2	13	6	4	98	0.2	0.98	2.30	0.73	1.37	0.43	0.00
2	13	7	10	155	0.13	1.55	0.68	-0.28	1.37	0.43	0.33
2	13	8	24	151	0.12	1.45	0.68	0.22	1.37	0.43	0.72
2	13	9	5	164	0.11	1.30	-0.08	0.40	1.37	0.43	0.73
2	13	10	12	176	0.12	1.08	0.68	-0.15	1.37	0.43	0.72
2	13	11	10	94	0.13	1.39	2.30	0.44	1.37	0.43	1.23
2	13	12	12	154	0.12	1.75	0.68	0.36	1.37	-0.67	0.29
2	13	13	4	174	0.1	0.56	0.68	0.82	1.37	-0.67	0.19
2	13	14	18	153	0.27	0.89	0.75	0.30	1.37	0.43	1.33
2	13	15	10	172	0.22	0.58	0.77	0.34	1.37	0.43	0.47
2	13	16	8	177	0.08	0.88	0.68	0.45	1.37	-0.67	1.41
2	13	17	2	125	0.09	2.71	0.68	0.34	1.37	-0.67	0.67
2	13	18	8	95	0.07	1.62	2.30	0.45	1.37	-0.67	1.41
2	13	19	7	173	0.07	1.27	0.68	0.45	1.37	-0.67	0.74
2	13	20	7	128	0.1	1.78	0.68	0.38	1.37	0.43	-0.02
2	13	21	16	111	0.2	1.15	2.30	0.59	1.37	-0.67	0.18
2	13	22	10	152	0.11	1.78	0.68	0.46	1.37	-0.67	0.90
2	13	23	4	162	0.07	1.30	0.68	0.46	1.37	-0.67	0.05
2	14	1	46	475	0.08	-0.74	-0.89	-0.78	-0.76	-0.67	0.52
2	14	2	36	469	0.16	-0.24	-0.94	-0.83	-0.76	-0.67	0.94
2	14	3	19	439	0.18	-0.52	-0.95	-0.74	-0.05	-0.67	0.60
2	14	4	13	459	0.15	0.21	-1.04	-0.93	-0.76	-0.67	0.60
2	14	5	24	501	0.14	-0.59	-1.00	-1.16	-0.89	-0.67	0.87
2	14	6	44	480	0.13	-0.63	-0.93	-0.78	-0.76	-0.67	0.98
2	14	7	35	493	0.13	-0.68	-0.91	-1.17	-0.98	-0.67	0.42
2	14	8	19	451	0.1	-0.20	-0.95	-0.74	-0.76	-0.67	-0.06
2	14	9	19	483	0.14	-0.72	-1.03	-0.93	-0.85	-0.67	-0.07
2	14	10	34	488	0.13	-0.61	-1.27	-0.93	-0.76	-0.67	0.54
2	14	11	11	455	0.18	-0.85	-0.89	-0.17	-0.69	-0.67	0.45
2	14	12	37	462	0.1	-0.26	-0.91	-0.85	-0.76	-0.67	0.39
2	14	13	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	14	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	15	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	16	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	17	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	18	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	19	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	20	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	21	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	22	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	23	0	NA	NA	NA	NA	NA	NA	NA	NA
2	15	1	13	341	0.17	0.19	-0.92	-0.52	-0.05	0.43	-0.93
2	15	2	16	361	0.16	0.24	0.68	-0.91	-0.45	-0.67	-1.62
2	15	3	5	456	0.11	-0.29	-0.89	-0.70	-0.76	-0.67	-1.12
2	15	4	26	461	0.2	-0.35	-0.08	-1.04	-0.81	-0.67	-1.59
2	15	5	16	444	0.3	-0.40	0.68	-1.15	-0.94	-0.61	-1.48
2	15	6	15	486	0.08	-0.50	-0.89	-0.92	-0.76	-0.67	-1.66
2	15	7	5	203	0.2	0.69	-1.12	-0.43	1.37	-0.23	-1.58
2	15	8	9	426	0.33	-0.40	-0.75	-0.77	-0.68	0.43	-1.23
2	15	9	14	369	0.19	0.28	-0.92	-0.38	-0.05	-0.67	-0.90
2	15	10	11	464	0.07	-0.06	-0.89	-0.82	-0.76	-0.67	-1.66
2	15	11	5	460	0.11	0.54	-0.89	-0.88	-0.76	-0.67	-1.70
2	15	12	7	410	0.17	0.51	-0.08	-0.92	-0.76	-0.67	-1.57
2	15	13	17	352	0.15	0.35	-0.89	-0.72	-0.13	0.43	-1.66
2	15	14	11	420	0.08	0.02	-1.03	-0.67	-0.05	-0.67	-1.65
2	15	15	13	396	0.13	0.43	-0.89	-0.71	-0.05	-0.67	-1.63
2	15	16	4	379	0.23	0.75	-0.69	-0.65	-0.76	0.15	-0.67
2	15	17	7	322	0.21	0.47	0.24	-0.72	-0.66	0.43	-1.52
2	15	18	25	343	0.22	0.36	-0.08	-0.63	-0.05	-0.67	-1.53
2	15	19	15	496	0.09	-0.38	-1.04	-1.11	-0.76	-0.67	-1.63
2	15	20	14	380	0.3	0.03	-0.08	-0.55	-0.46	-0.67	-0.82
2	15	21	14	293	0.21	0.50	-0.08	-0.67	-0.05	0.43	-1.14
2	15	22	10	353	0.17	0.20	0.68	-0.81	-0.48	-0.67	-0.92
2	15	23	11	432	0.09	-0.35	-0.89	-0.80	-0.05	-0.67	-1.61
2	16	1	8	58	0.25	0.04	2.30	1.70	-0.05	2.64	-1.20
2	16	2	1	33	0	1.25	2.30	1.20	1.37	2.64	-0.94
2	16	3	13	90	0.39	0.07	2.30	0.48	-0.05	2.64	-0.99
2	16	4	5	230	0.11	0.87	-0.89	-0.67	-0.33	2.64	-1.64
2	16	5	11	163	0.15	-0.13	0.68	0.38	-0.05	2.64	-0.78
2	16	6	4	75	0.04	0.84	-0.89	1.20	1.37	2.64	-0.83
2	16	7	17	207	0.2	-0.62	0.68	0.36	-0.76	2.64	-1.01
2	16	8	20	135	0.14	-0.24	0.98	0.51	-0.05	2.64	-1.10
2	16	9	9	132	0.15	1.33	-0.08	-0.65	-0.29	2.64	-1.52
2	16	10	8	74	0.2	0.95	0.39	1.10	1.37	2.64	-0.65
2	16	11	8	284	0.05	-0.76	-0.08	0.51	-0.76	2.64	-1.16
2	16	12	5	83	0.04	0.78	-0.89	1.20	1.37	2.64	-0.43
2	16	13	5	105	0.07	-0.44	0.68	0.49	-0.05	2.64	-2.23
2	16	14	4	136	0.09	1.24	0.68	-0.64	-0.05	2.64	-0.87
2	16	15	2	106	0.03	1.56	0.68	-0.71	-0.76	2.64	-1.71
2	16	16	9	120	0.06	-0.01	0.68	1.23	-0.05	2.64	-0.72
2	16	17	7	67	0.21	0.47	0.41	1.49	1.37	2.64	-0.97
2	16	18	7	213	0.33	0.07	0.57	-0.21	-0.05	2.64	-0.36
2	16	19	5	160	0.15	0.43	0.68	0.62	-0.05	2.64	0.17
2	16	20	6	320	0.13	-0.58	0.68	-0.26	-0.76	2.64	-0.44
2	16	21	3	85	0.06	0.38	0.68	2.29	-0.05	2.64	-0.61
2	16	22	6	345	0.1	-0.82	-0.08	0.21	-0.76	2.64	-0.79
2	16	23	5	110	0.06	1.39	0.68	-0.67	-0.05	2.64	-1.61
2	17	1	6	440	0.09	-0.58	-1.27	0.12	-0.05	-0.67	1.29
2	17	2	10	416	0.13	-0.58	-0.93	-0.18	-0.05	-0.67	0.64
2	17	3	12	417	0.09	-0.24	-0.92	0.15	-0.05	-0.67	1.57
2	17	4	9	349	0.12	0.29	-0.93	0.23	-0.05	-0.67	-0.11
2	17	5	5	309	0.28	0.52	-0.89	0.35	-0.05	0.43	0.52
2	17	6	16	385	0.13	0.31	-0.89	-0.45	-0.05	-0.67	0.41
2	17	7	23	372	0.09	0.31	-0.91	0.03	-0.05	-0.67	0.86
2	17	8	21	403	0.09	-0.15	-1.27	0.09	-0.05	-0.67	0.85
2	17	9	14	366	0.13	-0.37	-0.92	0.45	-0.05	-0.67	-0.31
2	17	10	11	419	0.13	-0.26	-0.89	-0.36	-0.05	-0.67	1.02
2	17	11	13	389	0.11	-0.35	-0.95	0.02	-0.05	-0.67	0.00
2	17	12	17	392	0.06	-0.26	-0.89	0.05	-0.05	-0.67	0.62
2	17	13	4	362	0.17	0.15	-1.08	0.01	-0.05	-0.67	-0.67
2	17	14	9	424	0.17	0.37	-0.89	-0.29	-0.76	-0.67	0.90
2	17	15	12	395	0.09	-0.06	-1.27	-0.09	-0.05	-0.67	0.37
2	17	16	18	399	0.11	-0.17	-0.89	0.09	-0.05	-0.67	1.05
2	17	17	15	373	0.09	-0.59	-0.89	0.45	-0.05	-0.67	-0.58
2	17	18	11	357	0.09	0.20	-0.89	0.47	-0.05	-0.67	0.68
2	17	19	15	398	0.26	0.98	-0.89	-0.41	-0.57	-0.67	0.49
2	17	20	20	367	0.08	0.25	-0.91	0.05	-0.05	-0.67	0.46
2	17	21	3	438	0.08	-0.60	-0.89	0.04	-0.76	-0.67	0.48
2	17	22	6	429	0.08	-0.22	-0.89	-0.07	-0.76	-0.67	0.68
2	17	23	15	382	0.08	-0.35	-0.92	0.45	-0.05	-0.67	0.47
2	18	1	4	430	0.06	-0.12	2.30	-0.78	-0.76	-0.67	0.52
2	18	2	5	199	0.07	0.53	2.30	0.46	-0.05	0.43	0.62
2	18	3	4	276	0.04	-0.21	2.30	0.03	-0.05	-0.67	-0.48
2	18	4	1	225	0	0.74	2.30	-0.19	-0.05	-0.67	-0.79
2	18	5	4	340	0.08	0.42	2.30	-0.56	-0.76	-0.67	-0.64
2	18	6	16	286	0.15	0.56	2.30	0.00	-0.05	-0.67	1.49
2	18	7	5	242	0.04	0.41	2.30	0.45	-0.05	-0.67	0.35
2	18	8	7	187	0.09	0.61	2.30	0.31	-0.05	0.43	1.57
2	18	9	8	200	0.15	0.62	2.30	0.08	-0.05	0.43	0.04
2	18	10	5	226	0.04	0.41	2.30	0.05	-0.05	0.43	0.69
2	18	11	8	463	0.07	0.24	2.30	-0.78	-0.76	-0.67	1.30
2	18	12	9	522	0.08	-0.14	2.30	-1.19	-1.11	-0.67	1.33
2	18	13	2	115	0.08	1.74	2.30	1.23	-0.05	-0.67	-0.03
2	18	14	6	274	0.18	0.31	2.30	-0.06	-0.05	-0.67	0.15
2	18	15	3	229	0.06	0.55	2.30	-0.06	-0.05	0.43	1.14
2	18	16	10	254	0.13	0.67	2.30	0.22	-0.05	-0.67	0.87
2	18	17	4	407	0.13	0.00	2.30	-0.78	-0.76	0.43	0.94
2	18	18	7	246	0.08	0.16	2.30	-0.04	-0.05	0.43	1.57
2	18	19	4	339	0.04	-0.04	2.30	0.05	-0.76	-0.67	0.04
2	18	20	3	217	0.01	0.65	2.30	0.03	-0.05	0.43	0.69
2	18	21	3	476	0.14	-0.52	2.30	-0.45	-0.76	-0.67	1.29
2	18	22	0	NA	NA	NA	NA	NA	NA	NA	NA
2	18	23	0	NA	NA	NA	NA	NA	NA	NA	NA
2	19	1	6	391	0.18	0.52	-0.89	-0.34	-0.64	0.43	0.98
2	19	2	6	383	0.07	0.68	-0.89	-0.56	-0.76	0.43	0.10
2	19	3	21	470	0.12	-1.02	-0.91	-0.76	-0.76	0.43	0.53
2	19	4	11	348	0.2	0.08	-1.03	-0.10	-0.05	0.43	0.14
2	19	5	10	446	0.15	-1.12	-0.81	-0.30	-0.76	0.43	0.01
2	19	6	14	350	0.08	0.07	-0.89	0.05	-0.05	0.43	0.74
2	19	7	8	388	0.27	-0.53	-0.38	-0.56	-0.40	0.43	-0.28
2	19	8	7	467	0.11	-1.18	-0.89	-0.29	-0.76	0.43	0.88
2	19	9	6	425	0.09	-0.49	-0.89	-0.23	-0.76	0.43	0.58
2	19	10	9	406	0.1	-0.18	-0.08	-0.75	-0.76	0.43	0.63
2	19	11	10	400	0.16	-0.36	-0.89	-0.71	-0.05	0.43	0.33
2	19	12	9	347	0.21	-0.22	-0.89	0.49	-0.13	0.43	0.63
2	19	13	8	497	0.1	-0.83	-0.89	-1.19	-1.11	0.43	0.44
2	19	14	16	378	0.12	-0.25	-1.03	-0.12	-0.05	0.43	0.74
2	19	15	16	418	0.15	-0.73	-0.08	-0.41	-0.76	0.43	0.82
2	19	16	22	448	0.15	-0.21	-0.94	-0.82	-0.76	0.43	0.77
2	19	17	13	485	0.15	-0.74	-0.95	-0.83	-0.76	0.43	1.20
2	19	18	13	447	0.14	-0.52	-0.98	-0.75	-0.76	0.43	0.17
2	19	19	10	412	0.21	-0.48	-0.81	-0.43	-0.05	0.43	1.25
2	19	20	11	355	0.16	-0.61	-0.89	0.20	-0.05	0.43	-0.14
2	19	21	9	390	0.1	-0.66	-0.89	-0.14	-0.05	0.43	0.65
2	19	22	10	401	0.15	-0.52	-0.93	0.13	-0.05	0.43	1.40
2	19	23	11	487	0.12	-0.83	-1.27	-0.86	-0.76	0.43	0.75
2	20	1	5	73	0.02	-0.13	0.68	1.70	1.37	0.43	-2.46
2	20	2	3	69	0.02	-0.02	0.68	2.29	1.37	0.43	-1.98
2	20	3	3	65	0.19	-0.83	1.22	3.07	-0.05	-0.67	-2.46
2	20	4	7	79	0.03	0.82	2.30	1.21	1.37	0.43	-0.92
2	20	5	5	62	0.03	0.13	0.68	2.29	1.37	0.43	-2.32
2	20	6	3	80	0.04	-0.03	0.68	1.70	1.37	0.43	-2.19
2	20	7	3	71	0.03	0.47	2.30	1.70	1.37	0.43	-0.79
2	20	8	2	72	0	0.22	0.68	1.70	1.37	0.43	-2.46
2	20	9	12	35	0.14	0.15	2.30	2.29	1.37	0.43	-2.29
2	20	10	4	44	0.06	-0.03	0.68	3.07	1.37	0.43	-2.25
2	20	11	11	42	0.07	0.15	2.30	1.70	1.37	0.43	-2.39
2	20	12	1	31	0	0.57	2.30	3.30	1.37	0.43	-1.25
2	20	13	5	63	0.08	-0.29	0.68	2.29	1.37	0.43	-2.35
2	20	14	6	66	0.05	0.39	2.30	1.70	1.37	0.43	-1.23
2	20	15	1	68	0	0.31	0.68	2.29	1.37	0.43	-1.98
2	20	16	3	78	0.05	1.06	2.30	1.23	1.37	0.43	-0.62
2	20	17	4	27	0.05	0.19	2.30	3.07	1.37	0.43	-2.25
2	20	18	0	NA	NA	NA	NA	NA	NA	NA	NA
2	20	19	0	NA	NA	NA	NA	NA	NA	NA	NA
2	20	20	0	NA	NA	NA	NA	NA	NA	NA	NA
2	20	21	0	NA	NA	NA	NA	NA	NA	NA	NA
2	20	22	0	NA	NA	NA	NA	NA	NA	NA	NA
2	20	23	0	NA	NA	NA	NA	NA	NA	NA	NA
2	21	1	4	504	0.17	0.72	-0.89	-0.66	-0.76	2.64	1.36
2	21	2	5	245	0.17	0.97	0.68	-0.65	-0.62	2.64	1.03
2	21	3	6	313	0.16	-0.20	0.68	-0.40	-0.52	2.64	0.41
2	21	4	10	338	0.18	0.27	-0.89	-0.34	-0.05	2.64	0.32
2	21	5	4	375	0.13	0.16	-0.49	0.04	-0.76	2.64	0.66
2	21	6	6	298	0.15	1.35	-0.08	-0.45	-0.76	2.64	0.94
2	21	7	10	526	0.21	-0.31	-1.08	-0.88	-0.76	2.64	1.28
2	21	8	11	490	0.15	-0.83	-0.89	-0.25	-0.76	2.64	-0.33
2	21	9	6	393	0.19	1.39	-0.89	-0.56	-0.76	2.64	-0.04
2	21	10	8	359	0.11	0.49	-0.89	-0.39	-0.05	2.64	0.95
2	21	11	6	295	0.1	0.19	-0.08	-0.29	-0.05	2.64	0.75
2	21	12	7	376	0.09	-0.05	-0.89	-0.19	-0.05	2.64	0.77
2	21	13	18	205	0.2	0.58	0.68	-0.13	-0.13	2.64	0.65
2	21	14	6	186	0.18	0.55	0.55	0.01	-0.05	2.64	1.50
2	21	15	6	328	0.14	0.27	-0.89	-0.73	-0.05	2.64	-0.76
2	21	16	2	215	0.1	0.75	-0.08	-0.46	-0.05	2.64	-0.35
2	21	17	5	408	0.12	-0.53	-0.08	-0.29	-0.76	2.64	0.19

Now let’s check the compression summary for HVT (map C). The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.

mapC_compression_summary <- map_C[[3]]$compression_summary %>%  dplyr::mutate_if(is.numeric, funs(round(.,4)))
compressionSummaryTable(mapC_compression_summary)

segmentLevel	noOfCells	noOfCellsBelowQuantizationError	percentOfCellsBelowQuantizationErrorThreshold	parameters
1	23	0	0	n_cells: 23 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans
2	508	434	0.85	n_cells: 23 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans

As it can be seen from the table above, 0% of the cells have hit the quantization threshold error in level 1 and 85% of the cells have hit the quantization threshold error in level 2

Let’s plot the Voronoi tessellation for layer 2 (map C)

muHVT::plotHVT(map_C,
        line.width = c(0.4,0.2), 
        color.vec = c("#141B41","#0582CA"),
        centroid.size = 0.1,
        maxDepth = 2)

Figure 14: The Voronoi Tessellation for layer 2 (map C) shown for the 100 cells in the dataset ’computers’ at level 2

Heat Maps

Now let’s plot all the features for each cell at level two as a heatmap for better visualization.


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "n",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 15: The Voronoi Tessellation with the heat map overlaid for features No. of entities in each cell


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "price",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 16: The Voronoi Tessellation with the heat map overlaid for features price in the ’computers’ dataset


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "speed",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 17: The Voronoi Tessellation with the heat map overlaid for features speed in the ’computers’ dataset


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "hd",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 18: The Voronoi Tessellation with the heat map overlaid for features hd in the ’computers’ dataset


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "ram",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 19: The Voronoi Tessellation with the heat map overlaid for features ram in the ’computers’ dataset


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "screen",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 20: The Voronoi Tessellation with the heat map overlaid for features screen in the ’computers’ dataset


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "ads",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 21: The Voronoi Tessellation with the heat map overlaid for features ads in the ’computers’ dataset

We now have the set of maps (map A, map B & map C) which will be used to predict which map and cell each test record is assigned to, but before that lets view our test dataset

6 Prediction on Test Data

Now once we have built the model, let us try to predict using our test dataset which cell and which layer each point belongs to.

Raw Testing Dataset

The testing dataset includes the following columns:

Price: The price of 486 PCs in US dollars.
Speed: The clock speed of the PCs in MHz.
HD: The size of the hard drive in MB.
RAM: The size of RAM in MB.
Screen: The size of the screen in inches.
Ads: The number of listings for 486 PCs’ prices for each month.

Let’s have a look at our randomly selected test dataset containing 1253 datapoints.

Table(head(testComputers_data))

Row.No	price	speed	hd	ram	screen	ads
3	1595	25	170	4	15	94
4	1849	25	170	8	14	94
7	1720	25	170	4	14	94
10	2575	50	210	4	15	94
11	2195	33	170	8	15	94
14	2295	25	245	8	14	94

The predictLayerHVT function is used to score the test data using the predictive set of maps. This function takes an input - a test data and a set of maps (map A, map B, map C).

Now, Let us understand the predictLayerHVT function.

predictLayerHVT(data,
                map_A,
                map_B,
                map_C,
                mad.threshold = 0.2,
                normalize = T, 
                distance_metric="L1_Norm",
                error_metric="max",
                child.level = 1, 
                line.width = c(0.6, 0.4, 0.2),
                color.vec = c("#141B41", "#6369D1", "#D8D2E1"),
                yVar= NULL,
                ...)

Each of the parameters of predictLayerHVT function has been explained below:

data - A dataframe containing the test dataset. The variables from this dataset can also be used to overlay as heatmap.
map A - A list of hvt.results.model obtained from HVT function while performing hierarchical vector quantization on train data.
map B - A list of hvt.results.model obtained from HVT function while performing hierarchical vector quantization on data with novelty which was obtained as a result of removeNovelty function.
map C - A list of hvt.results.model obtained from HVT function while performing hierarchical vector quantization on data without novelty obtained as a result of removeNovelty function.
child.level - A number indicating the layer for which the heat map is to be plotted.(Only used if hmap.cols is not NULL).It specifies the specific layer within the hierarchical structure of the model.
mad.threshold - A numeric values indicating the permissible Mean Absolute Deviation.
normalize - A logical value indicating if the columns in your dataset should be normalized. Default value is TRUE, which means the columns will be normalized.
distance_metric - The distance metric can be ’Euclidean” or “Manhattan”. Euclidean is selected by default.
error_metric - The error metric can be “mean” or “max”. mean is selected by default.
yVar - Name of the dependent variable(s).
... - color.vec and line.width can be passed from here.

The function predicts based on the HVT maps - map A, map B and map C, constructed using HVT function. For each test record, the function will assign that record to Layer1 or Layer2. Layer1 contains the cell ids from map A and Layer 2 contains cell ids from map B (novelty map) and map C (map without novelty).

Prediction Algorithm

The prediction algorithm recursively calculates the distance between each point in the test dataset and the cell centroids for each level. The following steps explain the prediction method for a single point in the test dataset:

Calculate the distance between the point and the centroid of all the cells in the first level.
Find the cell whose centroid has minimum distance to the point.
Check if the cell drills down further to form more cells.
If it doesn’t, return the path. Or else repeat steps 1 to 4 till we reach a level at which the cell doesn’t drill down further.

Note : The prediction algorithm will not work if some of the variables used to perform quantization are missing. In the test dataset, we should not remove any features

Let’s see which cell and layer each point belongs to and check the Mean Absolute Difference.


validation_data <- testComputers
new_predict <- predictLayerHVT(
    data=validation_data,
    map_A,
    map_B,
    map_C,
    normalize = T
  )

summary_list <- map_A[[3]]

  train_colnames <- names(summary_list[["nodes.clust"]][[1]][[1]])
  scaled_test_data <- scale(
     testComputers[, train_colnames],
      center = summary_list$scale_summary$mean_data[train_colnames],
      scale = summary_list$scale_summary$std_data[train_colnames])

testComputers <- scaled_test_data
data1 <- data.frame(testComputers)
data1$Row.No <- row.names(testComputers_data)
data1 <- data1 %>% dplyr::select(Row.No,price,speed,hd,ram,screen,ads)
colnames(data1) <- c("Row.No","price_act","speed_act","hd_act","ram_act","screen_act","ads_act")
Layer2.Cell.ID <- new_predict$Layer2.Cell.ID
combined <- cbind(data1,Layer2.Cell.ID)
combined <- combined %>%mutate(Cell.ID = gsub("[BC]", "", Layer2.Cell.ID))
mapC_summary <- map_C[[3]]$summary

df1 <- combined
df1$Cell.ID <- as.numeric(df1$Cell.ID)
df2 <- mapC_summary
merged_df <- merge(df1, df2, by = "Cell.ID", all.x = TRUE)
sorted_df <- merged_df[order(merged_df$Row.No), ]
sorted_df <- sorted_df %>% select(price_act,speed_act,hd_act,ram_act,screen_act,ads_act,Layer2.Cell.ID,price,speed,hd,ram,screen,ads)
sorted_df$Row.No <- testComputers_data$Row.No

sorted_df <- sorted_df%>% dplyr::select(Row.No,price_act,speed_act,hd_act,ram_act,screen_act,ads_act,Layer2.Cell.ID,price,speed,hd,ram,screen,ads)
colnames(sorted_df) <- c("Row.No","price_act","speed_act","hd_act","ram_act","screen_act","ads_act","Layer2.Cell.ID","price_pred","speed_pred","hd_pred","ram_pred","screen_pred","ads_pred")
sorted_df$diff <- rowMeans(abs(sorted_df[, c("price_act","speed_act","hd_act","ram_act","screen_act","ads_act")] - sorted_df[, c("price_pred","speed_pred","hd_pred","ram_pred","screen_pred","ads_pred")]))
rownames(sorted_df) <- NULL
options(scipen = 999)
sorted_df %>% head(1000) %>%as.data.frame() %>%Table(scroll = T)

Row.No	price_act	speed_act	hd_act	ram_act	screen_act	ads_act	Layer2.Cell.ID	price_pred	speed_pred	hd_pred	ram_pred	screen_pred	ads_pred	diff
3	-1.0785679	-1.2710382	-0.9472574	-0.7589986	0.4307274	-1.7213059	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4690860
4	1.1382448	-0.0817113	-0.7914378	-0.7589986	2.6404120	-1.7213059	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.7270993
7	1.3374115	-0.0817113	0.1512706	1.3676416	0.4307274	-1.1163339	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5289052
10	-1.0629809	0.6794579	-0.7758559	-0.7589986	-0.6741149	-0.5382496	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3296222
11	-0.6455967	0.6794579	0.4473277	-0.0501185	-0.6741149	-0.5382496	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2260196
14	0.9286867	-0.8904536	2.2859986	2.7854017	0.4307274	-0.5382496	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3797882
15	1.0845564	-0.8904536	2.2859986	2.7854017	0.4307274	-0.5382496	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3538099
19	-0.8984519	0.6794579	0.0266149	-0.0501185	-0.6741149	-0.5382496	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2389879
22	-1.0716404	0.6794579	0.4940736	-0.0501185	-0.6741149	-0.5382496	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2627947
24	-1.3660608	-0.8904536	0.0421969	-0.7589986	0.4307274	-0.5382496	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3562045
28	-1.2638796	-0.8904536	0.0421969	-0.7589986	-0.6741149	-0.5382496	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2052541
29	-0.7252634	0.6794579	0.0266149	-0.0501185	-0.6741149	-0.5382496	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2255907
33	-0.5520749	0.6794579	0.0460923	-0.0501185	-0.6741149	-0.5382496	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2512092
39	-0.0480964	-0.8904536	-0.7758559	-0.7589986	-0.6741149	-1.1163339	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2549919
40	-0.4810676	0.6794579	0.4473277	-0.0501185	0.4307274	-0.5382496	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2696121
43	1.0776288	2.2969425	0.4473277	1.3676416	0.4307274	-0.5382496	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.5514687
46	-1.2448289	-0.0817113	-0.2850242	-0.7589986	0.4307274	-0.5382496	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3835282
48	-1.6448943	-0.8904536	0.0577788	-0.7589986	0.4307274	-0.5382496	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4052738
53	0.5649909	-0.0817113	1.1952617	1.3676416	0.4307274	-0.5382496	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.2459902
62	1.5106000	-0.8904536	2.2859986	2.7854017	0.4307274	-0.5382496	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.2926470
73	-1.1495752	-0.8904536	-0.7758559	-0.7589986	-0.6741149	-0.5382496	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.0862625
74	-0.4810676	-0.8904536	2.2859986	-0.0501185	-0.6741149	-0.5382496	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.6523298
80	0.1250921	-0.8904536	1.1952617	1.3676416	0.4307274	-0.8609013	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3872675
85	-0.8655461	0.6794579	0.0577788	-0.7589986	2.6404120	-0.8609013	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.3805670
86	-0.1260312	-0.0817113	-0.9472574	-0.7589986	-0.6741149	-1.1163339	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2648147
93	0.6533170	-0.8904536	2.2859986	2.7854017	0.4307274	-0.8609013	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4794584
99	-1.0006331	0.6794579	-0.7758559	-0.7589986	0.4307274	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4373282
100	-0.5105097	-0.8904536	0.4473277	-0.0501185	-0.6741149	-0.8609013	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.4045669
104	-0.7252634	0.6794579	0.0266149	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.1964053
106	-0.4239154	0.6794579	0.4473277	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2337811
110	0.1337515	-0.8904536	1.1952617	1.3676416	0.4307274	-0.8609013	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3858242
111	-0.3788864	0.6794579	1.2342166	-0.0501185	0.4307274	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3886050
115	-0.5538068	0.6794579	0.0266149	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2249814
118	0.8334330	2.2969425	1.2342166	1.3676416	0.4307274	-0.8609013	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.5488108
133	-1.1651622	0.6794579	0.4473277	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2414053
157	0.8178461	-0.0817113	-0.2850242	-0.0501185	-0.6741149	-1.1163339	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3702157
158	0.1909037	2.2969425	-0.5577085	-0.7589986	-0.6741149	-0.8609013	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.5216904
160	-1.2119230	-0.8904536	0.0577788	-0.7589986	2.6404120	-0.8609013	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.6856320
168	-0.2212849	0.6794579	2.2859986	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.5739980
174	-0.9677273	0.6794579	0.0460923	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2181021
175	-0.6455967	0.6794579	0.4473277	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.1968342
176	-0.6300097	-0.8904536	0.4473277	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4168352
177	-0.6109590	0.6794579	0.4940736	-0.0501185	2.6404120	-0.8609013	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.1514702
184	-0.1208356	0.6794579	1.2342166	-0.0501185	2.6404120	-0.8609013	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.1931402
188	-1.0872273	-0.8904536	-0.7758559	-0.7589986	0.4307274	-0.8609013	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2322443
189	-1.4180174	0.6794579	0.0577788	-0.7589986	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3517822
190	1.6924480	0.6794579	0.4940736	-0.0501185	0.4307274	-1.1163339	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3869864
202	-1.2985173	0.6794579	0.0577788	-0.7589986	0.4307274	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3480365
205	-0.2472631	0.6794579	2.5976378	-0.0501185	0.4307274	-0.8609013	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.6137011
208	-0.6923576	-0.0817113	0.0577788	-0.7589986	2.6404120	-0.8609013	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.4729947
210	1.1780781	2.2969425	1.2342166	1.3676416	0.4307274	-0.8609013	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.6062516
221	-0.8135895	-0.0817113	0.5135511	-0.7589986	2.6404120	-0.8609013	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.4279274
225	-1.0006331	0.6794579	-0.7758559	-0.7589986	-0.6741149	-0.8609013	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3416838
226	-0.5434155	0.6794579	0.4473277	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2138644
228	0.2359327	-0.8904536	1.1952617	1.3676416	0.4307274	-0.8609013	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3687940
233	1.0845564	-0.8904536	2.2859986	2.7854017	0.4307274	-0.8609013	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4075852
235	-1.2448289	-0.0817113	0.0577788	-0.7589986	-0.6741149	-0.8609013	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3084121
237	0.7399113	-0.0817113	-0.6356183	-0.0501185	0.4307274	-1.1163339	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4386379
245	-0.9053794	-0.8904536	0.4473277	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4155113
247	-0.5676619	-0.8904536	0.4473277	-0.0501185	-0.6741149	-0.8609013	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.4140923
248	0.2099545	0.6794579	0.0460923	-0.0501185	2.6404120	-0.8609013	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.1108041
254	-0.3442487	0.6794579	2.2859986	-0.0501185	0.4307274	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.5696750
282	-0.8031982	-0.8904536	0.4473277	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3984811
283	-0.5676619	-0.8904536	0.4473277	-0.0501185	0.4307274	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4433974
285	-1.4249449	-0.8904536	-0.7758559	-0.7589986	-0.6741149	-0.8609013	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1227157
308	-0.7321909	-0.8904536	0.4473277	-0.0501185	-0.6741149	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3998050
310	-1.3227637	-0.8904536	-0.7758559	-0.7589986	-0.6741149	-0.8609013	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1056855
312	-0.7321909	0.6794579	0.4940736	-0.0501185	0.4307274	-0.8609013	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2063638
313	-0.3788864	-0.8904536	-0.9472574	-0.7589986	-0.6741149	-1.1163339	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2875199
320	-0.8205171	-0.8904536	-0.7758559	-0.7589986	-0.6741149	-0.8609013	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1427758
325	0.0350341	2.2969425	0.5135511	-0.0501185	2.6404120	-0.9415643	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.3031879
329	0.4801285	0.6794579	-0.1915325	-0.0501185	-0.6741149	-0.9415643	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3628656
330	-0.8135895	-0.0817113	0.0577788	-0.7589986	2.6404120	-0.9415643	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.4797562
332	-0.4897270	0.6794579	2.2859986	-0.0501185	-0.6741149	-0.9415643	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.5427015
348	-1.3227637	-0.0817113	-0.2850242	-0.7589986	-0.6741149	-0.9415643	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2777112
349	0.2688386	0.6794579	1.1952617	1.3676416	0.4307274	-0.9415643	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3438255
355	-0.5001183	0.6794579	2.2859986	-0.0501185	-0.6741149	-0.9415643	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.5409696
356	-0.4741401	-0.0817113	0.4473277	-0.0501185	-0.6741149	-0.9415643	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3214669
360	-1.2361694	0.6794579	-0.2850242	-0.7589986	-0.6741149	-0.9415643	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3901404
380	1.2421579	-0.0817113	-0.6356183	-0.0501185	2.6404120	-1.1163339	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.5235372
389	0.2532516	-0.8904536	-0.7836469	-0.0501185	2.6404120	-0.9415643	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4589957
397	0.3052081	0.6794579	2.2859986	-0.0501185	2.6404120	-0.9415643	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.3414235
399	0.2082226	2.2969425	0.5135511	-0.0501185	2.6404120	-0.9415643	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.2994321
405	0.1250921	-0.0817113	1.1952617	1.3676416	0.4307274	-0.9415643	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.2659209
406	0.3918024	-0.0817113	1.1952617	1.3676416	0.4307274	-0.9415643	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.2578524
417	-0.3875458	0.6794579	0.4473277	-0.0501185	-0.6741149	-0.9415643	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2532865
424	-0.1208356	1.1076156	0.5135511	-0.0501185	2.6404120	-0.9415643	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.1309450
425	0.1320196	2.2969425	0.4473277	-0.0501185	-0.6741149	-0.9415643	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5288339
430	-0.3788864	2.2969425	0.0266149	-0.0501185	-0.6741149	-0.9415643	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5138017
437	1.5106000	2.2969425	2.2859986	2.7854017	0.4307274	-0.9415643	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.5882169
446	0.6186793	-0.0817113	-0.6356183	-0.0501185	0.4307274	-1.1163339	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4184326
452	-0.0342413	2.2969425	0.0266149	-0.0501185	-0.6741149	-0.9415643	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4896149
454	0.6533170	0.6794579	2.2859986	2.7854017	0.4307274	-0.9415643	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4516722
455	-0.6404010	0.6794579	0.5135511	-0.7589986	2.6404120	-0.9415643	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.2643263
463	1.9435713	2.2969425	2.2859986	2.7854017	2.6404120	-0.9415643	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.7950152
477	0.8178461	-0.0817113	2.2859986	2.7854017	0.4307274	-0.9415643	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3306904
488	-0.8118576	0.6794579	0.5135511	-0.0501185	0.4307274	-0.9415643	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2231732
493	0.9130997	2.2969425	2.2859986	2.7854017	0.4307274	-0.9415643	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.5547526
495	1.1780781	2.2969425	1.2342166	1.3676416	0.4307274	-0.9415643	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.5928078
497	-0.8984519	0.6794579	-0.5577085	-0.7589986	-0.6741149	-0.9415643	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3812130
499	-0.5538068	0.6794579	0.0266149	-0.0501185	-0.6741149	-0.9415643	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2384252
501	-0.5520749	-0.8904536	-0.9472574	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2009689
509	-0.5520749	0.6794579	0.0460923	-0.0501185	-0.6741149	-0.9415643	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2354676
510	-1.1738216	0.6794579	-0.2850242	-0.7589986	-0.6741149	-0.9415643	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3816605
512	-0.8984519	0.6794579	0.0266149	-0.0501185	-0.6741149	-0.9415643	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2232463
515	1.1347810	2.2969425	2.2859986	2.7854017	0.4307274	-0.7936822	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.6163465
516	-0.7252634	0.6794579	0.0266149	-0.0501185	-0.6741149	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.1852021
530	-0.1901109	1.1076156	0.5135511	-0.0501185	2.6404120	-0.7936822	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.1671379
538	-1.4180174	-0.8904536	-0.7914378	-0.7589986	-0.6741149	-0.7936822	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1129549
540	-0.6750387	0.6794579	-0.5577085	-0.7589986	-0.6741149	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3515396
554	-0.7096764	-0.0817113	0.5135511	-0.7589986	2.6404120	-0.7936822	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.4218117
556	-0.4221835	0.6794579	0.5135511	-0.0501185	2.6404120	-0.7936822	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.1344571
557	3.0779559	-0.8904536	0.1512706	-0.0501185	-0.6741149	-1.7213059	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.7242998
565	-0.2126255	-0.8904536	-0.6356183	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2270607
566	-0.0169225	2.2969425	0.5135511	-0.0501185	2.6404120	-0.7936822	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.3364943
571	0.3848748	2.2969425	1.7016753	1.3676416	0.4307274	-0.7936822	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.4073445
580	-0.5676619	1.1076156	1.2342166	-0.0501185	0.4307274	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4172989
585	-0.1260312	0.6794579	-0.1915325	-0.0501185	-0.6741149	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3214320
591	-0.9937055	0.6794579	-0.2850242	-0.7589986	-0.6741149	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3269942
593	-0.7252634	2.2969425	0.0460923	-0.0501185	-0.6741149	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4515366
595	-1.4249449	0.6794579	-0.2850242	-0.7589986	-0.6741149	-0.7936822	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3969560
596	0.1683892	1.1076156	2.5976378	1.3676416	0.4307274	-0.7936822	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.4002201
597	-1.1651622	1.1076156	0.4940736	-0.0501185	0.4307274	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3255237
619	-1.3383506	0.6794579	0.4473277	-0.0501185	-0.6741149	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2590669
622	-0.5676619	0.6794579	-1.1926732	-1.1134387	0.4307274	-0.6189125	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.5411040
628	-1.3141043	-0.0817113	0.0460923	-0.7589986	-0.6741149	-0.7936822	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3068071
629	-1.2448289	-0.0817113	0.0577788	-0.7589986	0.4307274	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4104979
635	-1.3227637	0.6794579	-0.2850242	-0.7589986	-0.6741149	-0.7936822	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3799257
644	-0.9053794	0.6794579	1.2342166	-0.0501185	0.4307274	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3342241
645	-1.2448289	0.6794579	-0.1876370	-0.0501185	0.4307274	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3082084
650	0.2203458	2.2969425	1.7016753	1.3676416	0.4307274	-0.7936822	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.3799230
657	-1.7713219	-0.0817113	-0.2850242	-0.7589986	-0.6741149	-0.7936822	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3278239
662	-0.7841475	0.6794579	0.4940736	-0.0501185	2.6404120	-0.7936822	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.1915382
679	-1.0629809	2.2969425	-0.2850242	-0.7589986	-0.6741149	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.6081209
681	1.2508173	0.6794579	2.2859986	2.7854017	2.6404120	-0.7936822	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.6308153
685	-1.2604158	-0.8904536	-1.1926732	-1.1134387	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2087229
687	-1.4093579	-0.0817113	-0.2850242	-0.7589986	-0.6741149	-0.7936822	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2674966
692	0.1822443	0.6794579	1.7016753	1.3676416	0.4307274	-0.7936822	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.2703894
695	-1.2448289	-0.0817113	0.5135511	-0.7589986	0.4307274	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4138803
698	-0.8984519	0.6794579	-0.5577085	-0.7589986	-0.6741149	-0.7936822	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3565660
702	-0.9919737	0.6794579	0.4473277	-0.0501185	-0.6741149	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2707971
704	0.5580633	0.6794579	1.7016753	1.3676416	2.6404120	-1.2104407	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.5489497
709	1.2508173	0.6794579	2.2859986	2.7854017	2.6404120	-1.2104407	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.5613556
717	-1.1997999	1.1076156	0.4940736	-0.0501185	0.4307274	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4007564
722	-1.5115391	-0.0817113	-0.2850242	-0.7589986	-0.6741149	-1.2104407	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3539865
725	-0.6733068	0.6794579	0.0266149	-0.0501185	-0.6741149	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2633213
726	-0.2472631	-0.0817113	-0.6356183	-0.0501185	-0.6741149	-0.6189125	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3625372
728	0.2203458	2.2969425	1.7016753	1.3676416	0.4307274	-1.2104407	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.3104632
731	-0.5434155	2.2969425	0.4473277	-0.0501185	-0.6741149	-1.2104407	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.3714487
734	0.8749983	0.6794579	2.2859986	2.7854017	0.4307274	-1.2104407	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.4302017
736	-0.8118576	0.6794579	0.5135511	-0.0501185	0.4307274	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2679860
737	-0.8897925	-0.0817113	0.4473277	-0.0501185	-0.6741149	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3363796
749	-0.9053794	0.6794579	0.4473277	-0.0501185	-0.6741149	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2563647
751	-0.8031982	0.6794579	0.4473277	-0.0501185	-0.6741149	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2393345
762	-1.6847276	0.6794579	-0.2850242	-0.7589986	-0.6741149	-1.2104407	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.5097128
766	0.8178461	2.2969425	1.7016753	1.3676416	2.6404120	-1.2104407	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.7783274
770	0.7554982	0.6794579	2.2859986	2.7854017	0.4307274	-1.2104407	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.4482254
775	0.1337515	-0.0817113	-0.7836469	-0.0501185	0.4307274	-0.6189125	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3965454
784	-0.5971039	2.2969425	0.4473277	-0.0501185	-0.6741149	-1.2104407	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.3625006
801	0.4420270	2.2969425	1.7016753	1.3676416	0.4307274	-1.2104407	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.3474101
806	0.4714691	-0.0817113	1.7016753	1.3676416	2.6404120	-1.2104407	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.6210022
824	-0.1624008	2.2969425	0.5135511	-0.0501185	2.6404120	-1.2104407	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.3710103
826	-0.8551548	-0.0817113	0.5135511	-0.7589986	2.6404120	-1.2104407	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.4563277
833	-1.4197492	0.6794579	-0.2850242	-0.7589986	-0.6741149	-1.2104407	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4655498
848	-1.0629809	2.2969425	-0.2850242	-0.7589986	-0.6741149	-1.2104407	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5601163
857	-1.1045462	0.6794579	0.4940736	-0.0501185	0.4307274	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3135211
865	-0.4741401	0.6794579	0.4473277	-0.0501185	-0.6741149	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2836669
868	-0.8274446	-0.0817113	0.4473277	-0.0501185	0.4307274	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3421593
869	-0.0480964	-0.8904536	-0.7758559	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2563984
876	-0.9919737	-0.0817113	0.4473277	-0.0501185	-0.6741149	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3534099
888	-1.4249449	-0.0817113	-0.2850242	-0.0501185	-0.6741149	-1.2104407	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4566268
889	-1.3470101	-0.0817113	-0.2850242	-0.7589986	0.4307274	-1.2104407	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4604853
895	-0.9400171	0.6794579	0.4940736	-0.0501185	0.4307274	-1.2104407	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2860996
902	-0.2143573	2.2969425	0.4473277	-0.0501185	-0.6741149	-1.2104407	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4262917
911	-0.9556041	0.6794579	-0.5577085	-0.7589986	-0.6741149	-1.2507722	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4252359
921	0.4801285	-0.0817113	2.2859986	2.7854017	0.4307274	-1.2507722	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4385113
937	0.9113679	-0.0817113	2.2859986	2.7854017	0.4307274	-1.2507722	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3666380
939	-1.1149375	-0.0817113	-0.5577085	-0.7589986	-0.6741149	-1.2507722	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2718187
940	-0.3511762	2.2969425	2.2859986	-0.0501185	-0.6741149	-1.2507722	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5997939
941	-0.8620823	-0.8904536	-0.9472574	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1493010
944	-0.8897925	-0.0817113	0.5135511	-0.7589986	2.6404120	-1.2507722	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.4688225
945	-1.5912058	0.6794579	-0.2850242	-0.7589986	-0.6741149	-1.2507722	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.5008478
947	-0.1104442	-0.0817113	1.7016753	1.3676416	0.4307274	-1.2507722	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.2934901
952	-0.1260312	0.6794579	1.7016753	1.3676416	0.4307274	-1.2507722	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3979503
955	-0.0169225	1.1076156	0.5135511	-0.0501185	2.6404120	-1.2507722	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.1552647
962	0.7312518	2.2969425	1.7016753	1.3676416	2.6404120	-1.2507722	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.7571731
965	-1.0629809	2.2969425	-0.2850242	-0.7589986	-0.6741149	-1.2507722	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5533944
966	-0.7166040	0.6794579	0.5135511	-0.7589986	2.6404120	-1.2507722	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.3186653
973	-0.6455967	2.2969425	0.4473277	-0.0501185	-0.6741149	-1.2507722	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.3476965
975	-0.9919737	0.6794579	0.4473277	-0.0501185	-0.6741149	-1.2507722	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2775190
977	-0.2056979	-0.8904536	-1.1420319	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2911609
991	-0.5520749	2.2969425	0.5135511	-0.0501185	0.4307274	-1.2507722	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4053875
992	-1.0006331	2.2969425	-0.2850242	-0.7589986	0.4307274	-1.2507722	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5961443
994	0.4420270	2.2969425	1.7016753	1.3676416	0.4307274	-1.2507722	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.3406882
996	-0.9763867	-0.0817113	0.4473277	-0.0501185	-0.6741149	-1.2507722	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3575339
1008	-0.8897925	0.6794579	0.4473277	-0.0501185	-0.6741149	-1.2507722	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2604888
1011	-1.1651622	2.2969425	-0.2850242	-0.7589986	-0.6741149	-1.2507722	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5704246
1012	1.0152810	2.2969425	2.2859986	2.7854017	0.4307274	-1.2507722	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.5202482
1013	0.6515851	2.2969425	1.7016753	1.3676416	0.4307274	-1.2507722	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.3756145
1018	-1.4336043	-0.0817113	-0.2850242	-0.7589986	0.4307274	-1.2507722	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4816396
1020	-0.9677273	0.6794579	-0.2850242	-0.7589986	2.6404120	-1.2507722	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.4829257
1036	1.8136799	-0.8904536	0.4940736	-0.0501185	2.6404120	-0.6189125	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.6040394
1038	-1.1668940	-0.0817113	-0.2850242	-0.7589986	-0.6741149	-1.2507722	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3086067
1039	-0.6975532	0.6794579	2.2859986	-0.0501185	-0.6741149	-1.2507722	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.5595984
1051	0.1406791	2.2969425	1.7016753	1.3676416	0.4307274	-1.2507722	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.3035119
1052	-0.2143573	2.2969425	0.4473277	-0.0501185	-0.6741149	-1.2507722	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4195698
1067	-0.6473286	-0.0817113	0.4473277	-0.0501185	-0.6741149	-1.2507722	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3441368
1076	0.1562660	2.2969425	1.7016753	-0.0501185	2.6404120	-1.2507722	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.5304318
1090	-1.7643943	-0.0817113	0.5135511	-0.7589986	-0.6741149	-1.2507722	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.5359469
1097	-0.7702924	0.6794579	1.7016753	-0.0501185	-0.6741149	-1.9767386	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4662247
1112	-0.3875458	2.2969425	1.7016753	-0.0501185	-0.6741149	-1.9767386	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4500887
1115	0.0471573	2.2969425	2.2859986	1.3676416	0.4307274	-1.9767386	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.1832159
1125	0.0471573	-0.8904536	-0.6356183	-0.0501185	0.4307274	-0.6189125	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4054435
1130	-1.0439302	2.2969425	2.2859986	-0.7589986	-0.6741149	-1.9767386	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5849315
1138	0.4801285	-0.0817113	3.0650965	2.7854017	0.4307274	-1.9767386	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.5874312
1141	-0.8187852	2.2969425	1.7016753	-0.0501185	-0.6741149	-1.9767386	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.3873786
1145	-0.6213503	0.6794579	0.4940736	-0.0501185	2.6404120	-1.9767386	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.3023912
1155	-1.1668940	-0.0817113	0.4473277	-0.7589986	-0.6741149	-1.9767386	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	2.2122567
1160	-0.7252634	0.6794579	1.2342166	-0.0501185	-0.6741149	-1.9767386	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.3958197
1172	-0.0480964	-0.0817113	3.0650965	1.3676416	0.4307274	-1.9767386	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.5180137
1174	0.6689040	1.1076156	3.0650965	2.7854017	0.4307274	-1.9767386	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.3577474
1177	0.0090558	-0.0817113	2.2859986	1.3676416	0.4307274	-1.9767386	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.3786387
1187	-0.5971039	2.2969425	1.7016753	-0.0501185	-0.6741149	-1.9767386	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4151624
1188	1.5192595	-0.8904536	-0.2850242	1.3676416	-0.6741149	-1.7213059	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5546423
1196	1.1642231	-0.8904536	0.1512706	1.3676416	0.4307274	-0.6189125	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.4470960
1197	-1.0716404	0.6794579	0.4940736	-0.0501185	-0.6741149	-1.9767386	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.3960171
1200	-1.0785679	-0.0817113	1.7016753	-0.0501185	-0.6741149	-1.9767386	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.6374643
1206	-1.3470101	0.6794579	0.4473277	-0.7589986	0.4307274	-1.9767386	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.5344366
1218	-0.9140388	-0.0817113	1.7016753	-0.0501185	0.4307274	-1.9767386	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.6596138
1220	-1.3383506	2.2969425	0.4473277	-0.7589986	-0.6741149	-1.9767386	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4309743
1221	0.4783966	2.2969425	2.2859986	1.3676416	0.4307274	-1.9767386	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.2108669
1228	-1.3383506	2.2969425	0.4473277	-0.7589986	-0.6741149	-2.2859465	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4825090
1242	-1.4959522	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.2859465	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	2.0887035
1247	-1.1738216	2.2969425	0.4473277	-0.7589986	0.4307274	-2.2859465	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5082286
1253	-0.8984519	0.6794579	0.4940736	-0.0501185	-0.6741149	-2.2859465	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4186870
1254	0.1337515	0.6794579	-0.7758559	-0.7589986	-0.6741149	-0.6189125	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4101111
1259	-0.4724082	2.2969425	0.4940736	-0.0501185	0.4307274	-2.2859465	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4271898
1269	-1.0439302	2.2969425	2.2859986	-0.7589986	-0.6741149	-2.2859465	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.6364662
1270	-1.3314231	0.6794579	0.4940736	-0.0501185	-0.6741149	-2.2859465	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4908489
1277	0.9113679	1.1076156	3.0650965	2.7854017	0.4307274	-2.2859465	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.3828876
1282	-0.9140388	0.6794579	1.7016753	-0.0501185	0.4307274	-2.2859465	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5878571
1283	-0.6473286	0.6794579	1.7016753	-0.0501185	-0.6741149	-2.2993903	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5404939
1285	-1.5981334	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.2993903	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	2.1034931
1287	-0.9850461	0.6794579	0.4940736	-0.0501185	-0.6741149	-2.2993903	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4353600
1288	-0.9140388	0.6794579	1.7016753	-0.0501185	0.4307274	-2.2993903	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5900977
1298	-0.1104442	0.6794579	2.2859986	1.3676416	0.4307274	-2.2993903	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.3254691
1302	-1.2084593	-1.2710382	-1.2978514	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2219506
1303	-1.3383506	0.6794579	2.2859986	-0.7589986	-0.6741149	-2.2993903	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.7677039
1305	0.1493385	2.2969425	2.2859986	1.3676416	0.4307274	-2.2993903	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.2199610
1310	-0.3875458	2.2969425	1.7016753	-0.0501185	-0.6741149	-2.2993903	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.5038640
1311	0.2688386	2.2969425	2.2859986	1.3676416	0.4307274	-2.2993903	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.2297158
1320	0.5580633	2.2969425	2.2859986	1.3676416	2.6404120	-2.2993903	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.6462007
1329	-0.5971039	2.2969425	1.7016753	-0.0501185	-0.6741149	-2.2993903	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4689377
1336	-0.9937055	2.2969425	0.4473277	-0.7589986	-0.6741149	-2.4472723	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4519557
1348	-0.3009516	2.2969425	1.7016753	-0.0501185	0.4307274	-2.4472723	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.5554195
1367	-1.4336043	0.6794579	0.4473277	-0.7589986	0.4307274	-2.4472723	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.6272913
1375	-0.3944734	0.6794579	1.7016753	-0.0501185	2.6404120	-2.4472723	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.5442676
1377	0.9130997	-0.8904536	0.4940736	-0.0501185	-0.6741149	-0.6189125	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.4510996
1382	-0.1104442	0.6794579	2.2859986	1.3676416	0.4307274	-2.4472723	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.3501161
1383	-1.5981334	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.4472723	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	2.1140561
1387	0.9113679	1.1076156	3.0650965	2.7854017	0.4307274	-2.4472723	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.4097753
1388	1.5677522	1.1076156	4.6232922	4.2031619	0.4307274	-2.4607161	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	0.9819822
1390	-0.8984519	0.6794579	0.8446676	-0.0501185	0.4307274	-2.4607161	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4715529
1393	-0.6542561	2.2969425	3.0650965	-0.0501185	-0.6741149	-2.4607161	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.7135368
1396	0.0090558	0.6794579	1.7016753	1.3676416	0.4307274	-2.4607161	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.3500863
1413	1.5192595	2.2969425	4.6232922	4.2031619	0.4307274	-2.4607161	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	1.1562635
1414	1.7773103	1.1076156	4.6232922	4.2031619	0.4307274	-2.4607161	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	1.0169086
1434	0.7018098	0.6794579	3.0650965	2.7854017	0.4307274	-2.4607161	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.5042857
1440	0.1250921	-0.0817113	-0.7758559	-0.7589986	0.4307274	-0.6189125	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3977861
1441	0.9113679	0.6794579	3.0650965	2.7854017	0.4307274	-2.4607161	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.4833755
1443	0.9823751	0.6794579	4.6232922	2.7854017	0.4307274	-2.4607161	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.7549094
1451	0.6533170	2.2969425	3.0650965	2.7854017	0.4307274	-2.4607161	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.5857557
1453	1.5677522	1.1076156	4.6232922	4.2031619	0.4307274	-2.4607161	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	0.9819822
1454	1.1642231	2.2969425	3.0650965	2.7854017	2.6404120	-2.4607161	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.6674233
1455	0.2982806	0.6794579	1.7016753	1.3676416	2.6404120	-2.4607161	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.7096484
1459	-0.6473286	2.2969425	0.4473277	-0.0501185	-0.6741149	-2.4607161	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.3818145
1461	1.0862882	2.2969425	4.6232922	2.7854017	0.4307274	-2.4607161	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.8456161
1480	-1.0785679	2.2969425	0.4473277	-0.0501185	-0.6741149	-2.4607161	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4056986
1483	0.8749983	2.2969425	3.0650965	2.7854017	0.4307274	-2.4607161	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.5507019
1486	-0.3338574	0.6794579	-0.9472574	-0.7589986	-0.6741149	-0.6189125	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3383311
1491	-1.1495752	0.6794579	0.4473277	-0.0501185	-0.6741149	-2.4607161	C185	-0.7912959	1.7279813	0.7575812	-0.2166340	-0.2811173	-1.7525245	0.4974602
1496	1.3460710	0.6794579	4.6232922	4.2031619	0.4307274	-2.4607161	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	1.0163949
1497	0.7554982	2.2969425	3.0650965	2.7854017	0.4307274	-2.4607161	C15	0.8693193	1.2680365	2.6053382	2.5710892	1.3300177	-1.7644512	0.5687255
1500	0.3069400	0.6794579	-0.6356183	-0.7589986	-0.6741149	-0.6189125	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4545456
1502	-0.5520749	-0.8904536	-0.6356183	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1704858
1504	-0.5538068	-0.8904536	-1.1030770	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2266502
1507	1.3374115	-0.0817113	0.1512706	1.3676416	0.4307274	-0.6189125	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.5091823
1508	0.6533170	-0.8904536	-0.7914378	-0.0501185	2.6404120	-1.7213059	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.6569287
1510	-0.5070459	-0.0817113	-0.9472574	-0.7589986	0.4307274	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4444599
1511	-1.0785679	-0.8904536	-0.9472574	-0.7589986	0.4307274	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2471404
1514	0.9650563	-0.0817113	-0.6356183	-0.0501185	2.6404120	-0.6189125	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4906051
1515	0.1250921	-0.0817113	-0.7758559	-0.7589986	-0.6741149	-0.6189125	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2995345
1519	0.6602445	-0.0817113	0.1434796	-0.0501185	-0.6741149	-0.6189125	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4054230
1525	0.4108531	-0.8904536	-0.6356183	-0.0501185	2.6404120	-0.6189125	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4068159
1527	-0.7321909	0.6794579	-1.1926732	-1.1134387	-0.6741149	-0.6189125	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4209170
1528	2.3332454	0.6794579	0.4940736	-0.0501185	2.6404120	-0.6189125	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.4345508
1530	0.2203458	-0.0817113	-0.6356183	-0.0501185	0.4307274	-0.6189125	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.4213515
1538	-1.4336043	-1.2710382	-1.1926732	-1.1134387	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3010184
1539	0.3242589	-0.0817113	-0.7914378	-0.0501185	-0.6741149	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2493188
1555	-1.1062781	-1.2710382	-1.1420319	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2044949
1558	-0.2056979	-0.8904536	-1.1420319	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2911609
1563	1.5106000	0.6794579	0.1512706	1.3676416	0.4307274	-0.6189125	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4111855
1570	-0.7321909	-1.2710382	-0.7758559	-0.7589986	-0.6741149	-0.6189125	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2058135
1576	1.3529985	0.6794579	-0.2655468	1.3676416	0.4307274	0.3624865	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2908216
1577	-0.5676619	-1.2710382	-0.7758559	-0.7589986	0.4307274	0.3624865	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2319777
1581	-0.0325094	0.6794579	-0.2850242	-0.7589986	-0.6741149	0.3624865	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2891217
1586	-0.2212849	0.6794579	-1.1926732	-1.1134387	-0.6741149	0.3624865	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2649954
1590	0.9113679	-0.8904536	0.1512706	1.3676416	-0.6741149	0.3624865	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1262013
1594	-0.5520749	-0.8904536	-0.7135281	-0.7589986	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0803848
1596	0.3588966	-0.0817113	-0.7914378	-0.0501185	-0.6741149	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2550918
1602	-0.5867126	-0.0817113	-0.7914378	-0.7589986	0.4307274	0.3624865	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2907739
1603	-0.5676619	-0.8904536	-1.1926732	-1.1134387	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1709408
1617	-0.8620823	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1203663
1624	-0.9140388	-0.8904536	-1.1926732	-1.1134387	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2286703
1626	-0.8118576	-0.8904536	-0.9472574	-0.7589986	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1116641
1633	-1.0872273	-1.2710382	-1.1926732	-1.1134387	-0.6741149	0.3624865	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2591844
1642	-0.1277631	-0.0817113	-1.1926732	-1.1134387	-0.6741149	0.3624865	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3128929
1645	1.0066215	0.6794579	0.1434796	-0.0501185	0.4307274	0.3624865	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2650227
1649	0.8334330	-0.8904536	-0.2655468	1.3676416	0.4307274	0.3624865	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3048784
1659	0.7381794	-0.8904536	-0.2850242	-0.0501185	-0.6741149	0.3624865	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2403967
1669	0.4870561	-0.8904536	-0.7836469	-0.0501185	-0.6741149	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3049914
1670	1.0066215	-0.8904536	-0.6745732	-0.7589986	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.3336032
1671	0.3138676	-0.0817113	-0.2850242	-0.0501185	-0.6741149	0.3624865	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2137875
1681	0.3069400	-0.0817113	-0.2850242	-0.0501185	0.4307274	0.3624865	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2052761
1683	-0.7321909	-0.8904536	-1.1926732	-1.1134387	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1983623
1688	-0.2126255	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1135150
1692	0.0125196	0.6794579	-0.6356183	-0.7589986	-0.6741149	0.3624865	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2497122
1695	-0.7321909	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1359666
1697	2.6016875	0.6794579	0.3382540	-0.0501185	-0.6741149	0.3624865	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3364201
1704	0.6533170	0.6794579	-0.2850242	-0.0501185	-0.6741149	0.3624865	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1899706
1728	0.6966141	-0.0817113	-0.2850242	-0.0501185	0.4307274	0.3624865	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2702218
1729	0.8265055	-0.8904536	-0.6550957	-0.0501185	-0.6741149	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3822895
1731	0.3918024	-0.8904536	-0.2850242	-0.0501185	-0.6741149	0.3624865	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1826672
1742	1.5106000	0.6794579	0.1512706	1.3676416	-0.6741149	0.3624865	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2855043
1745	-0.2143573	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1132263
1756	0.7918678	0.6794579	-0.2850242	-0.0501185	2.6404120	0.3624865	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3964077
1759	1.3529985	0.6794579	-0.3629340	-0.0501185	0.4307274	0.3624865	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3240230
1771	1.3460710	0.6794579	0.1512706	1.3676416	-0.6741149	0.3624865	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2580828
1772	0.8178461	0.6794579	-0.2850242	-0.0501185	-0.6741149	0.3624865	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2173921
1774	0.9113679	0.6794579	-0.2850242	-0.0501185	-0.6741149	0.3624865	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2329790
1784	-0.3875458	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1203119
1798	-1.2517564	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.3624865	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1570941
1800	-0.8118576	-1.2710382	-1.1420319	-0.7589986	-0.6741149	-1.7213059	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4120799
1811	-0.2992197	-0.8904536	-0.6356183	-0.0501185	0.4307274	0.3624865	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1841475
1813	-0.0411688	0.6794579	-1.1926732	-1.1134387	-0.6741149	0.3624865	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2950147
1818	2.4094483	0.6794579	0.4434322	-0.7589986	0.4307274	0.3624865	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3089213
1819	-0.8187852	-0.8904536	-0.7914378	-0.7589986	-0.6741149	0.3624865	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1118516
1824	2.3852019	0.6794579	0.3382540	-0.0501185	0.4307274	0.3624865	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2196485
1826	1.6041218	-0.0817113	0.1512706	1.3676416	-0.6741149	0.3624865	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3539800
1832	-1.2517564	-0.8904536	-0.7836469	-0.7589986	-0.6741149	1.0212337	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1014105
1845	0.2186139	-0.8904536	0.0499878	-0.0501185	-0.6741149	1.0212337	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1496275
1858	0.9979621	-0.8904536	0.4940736	1.3676416	-0.6741149	1.0212337	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1655488
1865	2.2189410	0.6794579	0.1434796	-0.0501185	2.6404120	1.0212337	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.2438877
1871	1.3460710	0.6794579	-0.2850242	-0.0501185	0.4307274	-1.7213059	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3298528
1872	0.8265055	0.6794579	-0.2850242	-0.7589986	-0.6741149	1.0212337	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2961914
1875	-0.9053794	-1.2710382	-0.7758559	-0.7589986	-0.6741149	1.0212337	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1741061
1876	-0.7321909	-0.0817113	-1.1926732	-1.1134387	-0.6741149	1.0212337	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2995067
1877	-1.0716404	-0.0817113	-0.9472574	-0.7589986	-0.6741149	1.0212337	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2514187
1889	3.9421665	-0.8904536	2.6755476	1.3676416	-0.6741149	1.0212337	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	1.0198285
1890	0.3918024	-0.8904536	0.0499878	-0.0501185	-0.6741149	1.0212337	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1784923
1892	0.6429257	0.6794579	-0.2850242	-0.0501185	2.6404120	1.0212337	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4039357
1897	0.4783966	-0.0817113	-1.1926732	-0.7589986	-0.6741149	1.0212337	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3931991
1901	-0.6109590	-1.2710382	-0.9472574	-0.7589986	2.6404120	1.0212337	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.5007895
1910	-0.0740747	0.6794579	-0.6356183	-0.0501185	0.4307274	1.0212337	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2503772
1919	-0.6386691	-1.2710382	-0.9472574	-0.0501185	-0.6741149	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4423148
1922	-0.7321909	-0.8904536	-0.9472574	-0.7589986	0.4307274	-1.7213059	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4633848
1929	-0.7408504	-0.8904536	-1.1926732	-1.1134387	0.4307274	1.0212337	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3560106
1930	-0.9053794	-0.8904536	-0.6356183	-0.7589986	-0.6741149	1.0212337	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1838114
1937	1.0862882	0.6794579	-0.2850242	-0.0501185	2.6404120	1.0212337	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4467515
1940	1.3529985	0.6794579	0.4940736	-0.0501185	0.4307274	1.0212337	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3180824
1941	2.2120134	-0.0817113	0.1512706	-0.0501185	-0.6741149	1.0212337	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3904415
1944	0.6446576	-1.2710382	0.4940736	1.3676416	-0.6741149	1.0212337	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2400317
1965	0.1337515	0.6794579	-0.6356183	-0.0501185	0.4307274	1.0212337	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2157395
1971	-1.2604158	-1.2710382	-1.1926732	-1.1134387	0.4307274	1.0212337	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3324035
1975	1.3443391	-0.8904536	0.4940736	1.3676416	-0.6741149	1.0212337	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2232783
1979	-2.0241771	-0.8904536	-1.2978514	-1.1134387	-0.6741149	1.0212337	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3023442
1986	-0.9053794	-0.8904536	-0.9472574	-0.7589986	-0.6741149	-1.7213059	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3005997
1998	0.6533170	-0.8904536	0.4940736	1.3676416	-0.6741149	1.0212337	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1751576
2007	-0.5607343	-0.8904536	-1.1926732	-1.1134387	-0.6741149	1.0212337	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2117200
2010	0.1406791	0.6794579	-1.1420319	-0.7589986	-0.6741149	1.0212337	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2961620
2014	-1.2517564	-0.8904536	-1.1926732	-1.1134387	-0.6741149	1.0212337	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1624117
2015	0.8247736	-1.2710382	0.4940736	1.3676416	-0.6741149	1.0212337	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2100123
2019	-1.2448289	-0.8904536	-1.1030770	-0.7589986	-0.6741149	1.0212337	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1137335
2020	-0.3858139	-1.2710382	0.0499878	-0.0501185	-0.6741149	1.0212337	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2066937
2023	3.0848834	0.6794579	0.1434796	-0.0501185	2.6404120	1.0212337	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3882114
2025	3.2511444	0.6794579	0.3382540	1.3676416	2.6404120	1.0212337	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4303807
2040	-1.0716404	0.6794579	-1.1030770	-0.7589986	-0.6741149	1.0212337	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2782722
2048	1.4309333	-0.0817113	-0.6550957	-0.0501185	-0.6741149	-1.7213059	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3679198
2054	1.3374115	-0.0817113	0.4940736	1.3676416	-0.6741149	1.0212337	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3344434
2056	-0.3511762	0.6794579	-0.7914378	-0.7589986	-0.6741149	1.0212337	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1729719
2061	3.7707099	-0.0817113	2.6755476	1.3676416	-0.6741149	1.0212337	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	1.0072463
2066	0.8334330	-0.8904536	-0.2655468	1.3676416	0.4307274	1.0212337	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3620957
2071	-0.5590024	-0.8904536	-0.7836469	-0.0501185	0.4307274	1.0212337	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2243028
2090	1.4309333	-0.8904536	0.4940736	1.3676416	-0.6741149	1.0212337	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2377106
2093	0.6602445	-0.0817113	-0.7135281	-0.7589986	-0.6741149	1.0212337	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3868376
2094	-0.8118576	-0.8904536	-0.7135281	-0.7589986	-0.6741149	1.0212337	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1656158
2097	-0.5607343	-1.2710382	-0.7758559	-0.0501185	-0.6741149	1.0212337	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2660686
2102	1.0066215	0.6794579	0.1434796	-0.0501185	0.4307274	1.0212337	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2771260
2103	-1.0196838	-1.2710382	-1.2978514	-0.0501185	-0.6741149	-1.7213059	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.5204847
2106	-0.3511762	-0.8904536	-0.6356183	-0.0501185	0.4307274	1.0212337	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2055441
2110	1.1728825	-0.0817113	0.4940736	1.3676416	-0.6741149	1.0212337	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3070218
2113	0.1250921	0.6794579	-0.7758559	-0.7589986	0.4307274	1.0212337	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3213397
2114	-0.3858139	-0.8904536	-0.7758559	-0.7589986	-0.6741149	1.0212337	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1265840
2116	2.3852019	0.6794579	0.1512706	-0.0501185	-0.6741149	1.0212337	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3234473
2118	-0.4741401	-0.0817113	-1.1926732	-1.1134387	-0.6741149	1.0212337	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2650090
2123	0.2445922	-0.8904536	-0.2850242	-0.0501185	2.6404120	1.0212337	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2192632
2124	0.9113679	0.6794579	0.0499878	-0.0501185	-0.6741149	1.0212337	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2297720
2126	0.1337515	-0.0817113	-0.2850242	-0.0501185	0.4307274	0.3221550	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2205069
2129	-0.2056979	-0.8904536	-1.1420319	-0.7589986	-0.6741149	0.3221550	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1522240
2133	0.6533170	-1.2710382	-0.6550957	-0.0501185	-0.6741149	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4168555
2134	0.1337515	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.3221550	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0905453
2142	-1.2604158	-1.2710382	-1.1926732	-1.1134387	-0.6741149	0.3221550	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2370416
2145	0.4870561	-0.0817113	-0.2850242	-0.0501185	0.4307274	0.3221550	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2420173
2146	-0.3788864	0.6794579	-0.9472574	-0.7589986	-0.6741149	0.3221550	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1739817
2149	0.6515851	-0.0817113	0.0499878	-0.0501185	-0.6741149	0.3221550	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2315532
2152	-0.8118576	-0.0817113	-0.7836469	-0.7589986	-0.6741149	0.3221550	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2307387
2156	-0.5001183	0.6794579	-0.7914378	-0.7589986	0.4307274	0.3221550	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2927816
2157	-0.4221835	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.3221550	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1520934
2158	-0.7079445	-0.0817113	-0.7914378	-0.7589986	0.4307274	0.3221550	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3177012
2161	0.0454254	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.3221550	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0758243
2164	-0.3788864	-0.8904536	-1.1420319	-0.0501185	-0.6741149	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3680494
2179	-1.4249449	-1.2710382	-0.9472574	-0.7589986	0.4307274	0.3221550	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3429802
2182	-0.0325094	-0.0817113	-0.7135281	-0.0501185	0.4307274	0.3221550	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3196344
2183	-0.9053794	-0.8904536	-1.1926732	-1.1134387	-0.6741149	0.3221550	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2339490
2188	-0.6473286	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.3221550	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1285448
2190	3.2511444	0.6794579	0.3382540	1.3676416	2.6404120	0.3221550	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3935489
2193	-0.9053794	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.3221550	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1715532
2205	0.4350995	-0.8904536	-0.6550957	-0.0501185	-0.6741149	0.3221550	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2582839
2207	-0.3009516	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.3221550	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1055158
2209	0.0454254	-0.0817113	-0.7758559	-0.7589986	-0.6741149	0.3221550	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2708561
2215	0.6256068	-0.8904536	-0.2850242	-0.7589986	-0.6741149	0.3221550	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3224593
2218	0.3502371	-0.8904536	-0.9472574	-0.0501185	0.4307274	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4077083
2221	0.2186139	-0.8904536	-1.1420319	-0.7589986	-0.6741149	0.3221550	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2229426
2228	1.6907161	0.6794579	0.0421969	1.3676416	0.4307274	0.3221550	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3025392
2230	0.1926356	-0.8904536	-0.2850242	-0.0501185	2.6404120	0.3221550	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.1993018
2235	-1.0872273	-0.8904536	-1.1926732	-1.1134387	0.4307274	0.3221550	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3904060
2248	0.4853242	-0.0817113	-0.7135281	-0.7589986	-0.6741149	0.3221550	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3545605
2254	-1.0716404	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.3221550	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2251638
2259	-0.8984519	0.6794579	-0.9472574	-0.7589986	-0.6741149	0.3221550	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2203138
2261	-2.1107713	-0.8904536	-1.2978514	-1.1134387	-0.6741149	0.3221550	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3791820
2263	-0.6473286	-0.8904536	-1.1926732	-1.1134387	-0.6741149	0.3221550	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1909405
2264	0.4714691	-0.0817113	0.0499878	-0.0501185	-0.6741149	0.3221550	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2015339
2276	0.6533170	-1.2710382	-0.2850242	1.3676416	-0.6741149	-1.7078621	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.6314472
2283	4.8167683	0.6794579	0.4356413	-0.0501185	2.6404120	0.3221550	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.6887207
2289	1.3374115	-0.0817113	0.4473277	1.3676416	0.4307274	0.3221550	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3305615
2295	1.2577448	-0.8904536	0.4473277	1.3676416	-0.6741149	0.3221550	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1505595
2296	0.6446576	-1.2710382	0.4473277	1.3676416	-0.6741149	0.3221550	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1817453
2300	-0.7772199	-0.8904536	-0.9472574	-0.7589986	-0.6741149	0.3221550	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1126131
2303	1.3460710	0.6794579	0.0421969	1.3676416	0.4307274	0.3221550	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2450983
2307	0.8178461	-0.0817113	-0.2850242	-0.0501185	0.4307274	0.3221550	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2971490
2308	1.1798100	-0.0817113	-0.3629340	-0.0501185	0.4307274	0.3221550	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3704613
2316	1.6041218	-0.0817113	0.4473277	1.3676416	-0.6741149	0.3221550	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3206087
2321	1.3460710	0.6794579	0.4473277	1.3676416	-0.6741149	0.3221550	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2430285
2323	-0.9053794	-0.8904536	-0.9472574	-0.7589986	-0.6741149	-1.7078621	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2983591
2326	-0.2143573	0.6794579	-1.1926732	-1.1134387	-0.6741149	0.3221550	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2728719
2331	1.5106000	0.6794579	0.4473277	1.3676416	0.4307274	0.3221550	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2325647
2332	0.8178461	-0.8904536	0.4473277	1.3676416	0.4307274	0.3221550	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2046355
2333	1.1728825	-0.0817113	0.4473277	1.3676416	-0.6741149	0.3221550	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2487355
2334	-0.5676619	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.3221550	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0793166
2335	0.6533170	-0.8904536	0.4473277	1.3676416	-0.6741149	0.3221550	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1168713
2336	-0.2126255	-0.8904536	-0.2850242	-0.0501185	0.4307274	0.3221550	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1953703
2340	5.5095223	0.6794579	3.0650965	4.2031619	2.6404120	0.8195764	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	1.5893476
2351	-0.2212849	-0.8904536	-0.7758559	-0.7589986	0.4307274	0.8195764	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1911115
2362	-0.0394370	-0.8904536	-0.7836469	-0.7589986	2.6404120	0.8195764	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2812271
2364	1.3529985	0.6794579	-0.3239791	-0.7589986	0.4307274	-1.7078621	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4510968
2380	0.6446576	0.6794579	0.0499878	-0.0501185	0.4307274	0.8195764	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1676072
2396	-0.8811330	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.8195764	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1740115
2401	0.2186139	-0.0817113	-0.2850242	-0.0501185	0.4307274	0.8195764	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1781350
2402	-0.0480964	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.8195764	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0756235
2404	2.0215061	0.6794579	3.7273297	-0.0501185	-0.6741149	0.8195764	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.8079427
2409	-1.4249449	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.8195764	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1034432
2410	0.9113679	-1.2710382	0.4473277	1.3676416	-0.6741149	0.8195764	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1731466
2419	0.1337515	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.8195764	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1018743
2420	-0.8984519	0.6794579	-0.9472574	-0.7589986	-0.6741149	0.8195764	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1898280
2429	-1.4249449	-1.2710382	-1.1926732	-1.1134387	-0.6741149	0.8195764	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1781120
2436	-0.8620823	-1.2710382	-0.9472574	-0.7589986	-0.6741149	-1.7213059	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3712467
2440	1.3460710	0.6794579	-0.6356183	-0.0501185	2.6404120	-1.7078621	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.5181517
2442	0.2532516	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.8195764	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1981869
2443	0.6446576	-1.2710382	0.4473277	1.3676416	-0.6741149	0.8195764	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1986311
2452	0.2705704	-0.8904536	-1.1420319	-0.7589986	-0.6741149	0.8195764	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2332044
2454	-0.0480964	-0.0817113	-0.7758559	-0.7589986	-0.6741149	0.8195764	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2247832
2456	-0.9053794	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.8195764	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1536080
2458	-0.2126255	0.6794579	-0.2850242	-0.7589986	0.4307274	0.8195764	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2622111
2462	-0.3944734	0.6794579	-1.1926732	-1.1134387	-0.6741149	0.8195764	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2123667
2472	0.1320196	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.8195764	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1015856
2479	1.6041218	0.6794579	0.4473277	1.3676416	-0.6741149	0.8195764	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2438723
2491	0.3069400	-0.0817113	-0.2850242	-0.7589986	2.6404120	0.8195764	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.1853942
2493	-0.1260312	0.6794579	-0.9472574	-0.7589986	-0.6741149	-1.7078621	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3903145
2494	-0.7252634	-0.8904536	-1.1420319	-0.7589986	-0.6741149	0.8195764	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1380184
2498	-0.9937055	-1.2710382	-1.1926732	-1.1134387	-0.6741149	0.8195764	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1985897
2510	0.3918024	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.8195764	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1448827
2532	0.8438243	0.6794579	0.5135511	-0.0501185	0.4307274	0.8195764	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2780622
2540	-1.5981334	-1.2710382	-0.9472574	-0.7589986	-0.6741149	0.8195764	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1311740
2549	-0.2126255	-0.0817113	-0.7758559	-0.7589986	-0.6741149	0.8195764	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1973617
2551	0.1320196	-0.0817113	-0.7758559	-0.7589986	-0.6741149	0.8195764	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2548026
2555	0.9633244	-0.8904536	-0.2850242	-0.7589986	-0.6741149	0.8195764	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3900745
2565	-0.7079445	-0.0817113	-0.7914378	-0.7589986	0.4307274	0.8195764	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3074258
2566	-0.7495098	-0.8904536	-0.2850242	-0.7589986	0.4307274	0.8195764	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1619970
2568	1.3460710	0.6794579	-0.2850242	-0.0501185	0.4307274	-1.7078621	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3276122
2569	1.6907161	0.6794579	0.4473277	1.3676416	-0.6741149	0.8195764	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2583046
2573	1.5106000	0.6794579	0.4473277	1.3676416	-0.6741149	0.8195764	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2282853
2575	0.9113679	-0.8904536	0.4473277	1.3676416	-0.6741149	0.8195764	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1097159
2582	0.8853896	0.6794579	-0.6550957	-0.7589986	0.4307274	0.8195764	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3540595
2591	0.3069400	0.6794579	-0.2850242	-0.0501185	0.4307274	0.8195764	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1151887
2595	-1.2604158	-1.2710382	-1.1926732	-1.1134387	-0.6741149	0.8195764	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1541380
2601	-0.5520749	-0.0817113	-0.2850242	-0.7589986	-0.6741149	0.8195764	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2400595
2603	0.0454254	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.8195764	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0871532
2604	-0.5590024	0.6794579	-1.1926732	-1.1134387	-0.6741149	0.8195764	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2047218
2607	-0.5590024	-1.2710382	-0.9472574	-0.7589986	2.6404120	0.8195764	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4585205
2609	-1.2448289	-1.2710382	-0.9472574	-0.7589986	-0.6741149	-1.7078621	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3258541
2611	2.4787237	0.6794579	0.1434796	-0.0501185	0.4307274	0.8195764	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2260325
2612	-1.4180174	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.8195764	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1022886
2613	-0.3858139	0.6794579	-0.2850242	-0.7589986	-0.6741149	0.8195764	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2179917
2614	0.7901359	-0.8904536	-0.6550957	-0.0501185	-0.6741149	0.8195764	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3287856
2615	-0.3944734	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.8195764	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0902327
2637	1.3374115	-0.0817113	0.4473277	1.3676416	-0.6741149	0.8195764	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2930428
2638	1.6976436	0.6794579	0.1434796	1.3676416	0.4307274	0.8195764	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2446486
2648	-0.7425822	-0.8904536	-0.6356183	-0.7589986	-0.6741149	0.8195764	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1334453
2649	-0.1277631	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.8195764	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1359830
2651	-1.1755535	-1.2710382	-0.7836469	-0.7589986	-0.6741149	0.8195764	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1072805
2652	0.1857081	0.6794579	-0.6356183	-0.0501185	-0.6741149	-1.7078621	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3735189
2654	0.8334330	-0.8904536	-0.6745732	-0.7589986	-0.6741149	0.8195764	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.3130628
2659	-1.3314231	-1.2710382	-0.7836469	-0.7589986	-0.6741149	0.8195764	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1015238
2660	-1.5981334	-1.2710382	-0.7836469	-0.7589986	-0.6741149	0.4969247	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1997508
2662	-0.9140388	-1.2710382	-0.7836469	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1425697
2676	-0.4395024	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1200787
2679	-0.0480964	-0.0817113	-1.1926732	-1.1134387	-0.6741149	0.4969247	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3037644
2680	2.2968758	0.6794579	-0.2850242	1.3676416	2.6404120	0.4969247	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.2339057
2681	-0.5070459	0.6794579	-0.2850242	-0.7589986	0.4307274	0.4969247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3103811
2684	-0.6802344	0.6794579	-0.2850242	-0.7589986	-0.6741149	0.4969247	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2304664
2694	0.0454254	-0.0817113	-0.7758559	-0.7589986	-0.6741149	0.4969247	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2417278
2695	0.0038602	-0.8904536	-0.6356183	-0.7589986	-0.6741149	-1.7078621	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2683436
2701	-0.2056979	-0.8904536	-1.1420319	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1230957
2713	-0.9850461	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1865353
2715	0.4870561	0.6794579	0.0421969	-0.0501185	0.4307274	0.4969247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1391417
2719	-0.0861979	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1108814
2720	0.8091866	0.6794579	0.5135511	-0.0501185	-0.6741149	0.4969247	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2568985
2725	-0.3009516	0.6794579	-1.1926732	-1.1134387	-0.6741149	0.4969247	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2293112
2727	0.1164327	0.6794579	-0.2850242	-0.0501185	0.4307274	0.4969247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1256840
2732	0.2532516	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1674563
2737	-0.6473286	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0994165
2755	-0.7252634	-0.8904536	-0.7135281	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0868432
2766	1.3529985	-0.0817113	-0.9472574	-0.7589986	-0.6741149	-1.7078621	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4465921
2768	0.3831430	-0.0817113	-0.2850242	-0.0501185	2.6404120	0.4969247	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.1790190
2771	-0.9053794	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1424249
2784	1.6041218	-0.0817113	0.4473277	1.3676416	-0.6741149	0.4969247	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2914804
2794	-0.0567558	-1.2710382	0.4473277	-0.0501185	-0.6741149	0.4969247	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1438113
2796	0.4853242	-0.0817113	-0.3629340	-0.0501185	0.4307274	0.4969247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2255854
2814	-0.8118576	-0.0817113	-0.6550957	-0.7589986	0.4307274	0.4969247	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2831681
2816	2.0301655	0.6794579	0.4473277	1.3676416	-0.6741149	0.4969247	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3279160
2818	0.2445922	-0.8904536	-0.6550957	-0.0501185	-0.6741149	0.4969247	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1974044
2821	-0.2922922	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0765323
2830	-0.0480964	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1185301
2831	-0.0394370	-0.8904536	-0.9472574	-0.0501185	0.4307274	-1.7078621	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3750230
2839	0.2186139	0.6794579	-0.7758559	-0.7589986	-0.6741149	0.4969247	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2382820
2843	-0.9053794	-0.8904536	-0.2850242	-0.7589986	0.4307274	0.4969247	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1691224
2844	0.1406791	0.6794579	-1.1420319	-0.7589986	-0.6741149	0.4969247	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2639100
2857	2.0457525	0.6794579	0.4434322	-0.7589986	0.4307274	0.4969247	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2258989
2863	-1.2604158	-1.2710382	-1.1926732	-1.1134387	0.4307274	0.4969247	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3656806
2868	-0.8984519	-1.2710382	-0.7836469	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1399719
2878	0.4714691	-0.0817113	-0.7758559	-0.7589986	-0.6741149	0.4969247	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3127351
2895	-0.3858139	-1.2710382	0.0499878	-0.0501185	-0.6741149	0.4969247	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1326269
2897	0.1337515	0.6794579	-0.2850242	-0.0501185	0.4307274	0.4969247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1227975
2902	0.8178461	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.4969247	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1754328
2904	2.0457525	0.6794579	-0.7135281	-0.7589986	0.4307274	-1.7078621	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5576366
2909	1.0914839	0.6794579	-0.6550957	-0.7589986	0.4307274	0.4969247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3875085
2912	-1.2517564	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.4969247	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1346877
2914	0.9113679	-1.2710382	0.4473277	1.3676416	-0.6741149	0.4969247	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1271325
2918	-1.2517564	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.4969247	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1346877
2920	-0.7408504	-1.2710382	-1.1926732	-1.1134387	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2133495
2923	-0.5676619	-0.0817113	-1.1926732	-1.1134387	0.4307274	0.4969247	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3874417
2928	-0.8811330	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.4969247	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1692164
2932	0.4108531	-0.8904536	0.5135511	-0.0501185	-0.6741149	0.4969247	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1846652
2937	-0.2576545	-0.8904536	-0.2850242	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1782146
2957	-0.7166040	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1546005
2958	0.6602445	-0.0817113	-0.7914378	-0.7589986	-0.6741149	-1.7078621	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3051632
2964	-1.1149375	-1.2710382	-0.7836469	-0.7589986	-0.6741149	0.7120258	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1353083
2971	-0.3788864	-0.8904536	-0.6356183	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0995769
2983	2.9116949	0.6794579	0.4434322	1.3676416	2.6404120	0.7120258	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3398008
2988	-0.0325094	-0.0817113	-0.2850242	-0.0501185	0.4307274	0.7120258	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2020638
2998	0.7381794	0.6794579	0.0499878	-0.0501185	-0.6741149	0.7120258	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1493726
3005	-0.4741401	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0962787
3013	0.7554982	-0.8904536	-0.2850242	-0.7589986	-0.6741149	0.7120258	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3375118
3020	5.5095223	0.6794579	3.0650965	4.2031619	2.6404120	0.7120258	B1	1.0281464	1.1551887	3.0650965	2.7854017	2.6404120	-2.3416423	1.5714225
3021	1.2577448	-0.0817113	0.4473277	1.3676416	-0.6741149	0.7120258	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2618399
3030	0.9823751	-0.8904536	2.5197280	-0.0501185	-0.6741149	0.7120258	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.6368139
3031	0.6186793	-0.0817113	-0.7914378	-0.7589986	0.4307274	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3987279
3033	0.9113679	0.6794579	-0.6550957	-0.0501185	-0.6741149	-1.7078621	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4014194
3037	0.1770487	-0.8904536	0.4473277	-0.0501185	-0.6741149	0.7120258	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1571928
3039	0.9113679	-1.2710382	0.4473277	1.3676416	-0.6741149	0.7120258	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1552215
3041	-0.3355893	-1.2710382	-0.9472574	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1177407
3047	-0.7252634	-1.2710382	0.0421969	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2615533
3051	-0.5676619	-0.8904536	-0.7758559	-0.7589986	0.4307274	0.7120258	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1470683
3052	0.1250921	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1602004
3060	0.0454254	-1.2710382	0.0499878	-0.0501185	-0.6741149	0.7120258	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1104025
3089	-0.0480964	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1313356
3102	0.4714691	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.7120258	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1402354
3112	-0.5520749	-0.8904536	-0.6550957	-0.7589986	0.4307274	0.7120258	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1243438
3113	2.7315789	-0.8904536	0.1512706	-0.0501185	-0.6741149	-1.7078621	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.6643296
3115	0.7087373	0.6794579	0.0421969	-0.0501185	0.4307274	0.7120258	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1590635
3120	1.3460710	0.6794579	-0.2850242	1.3676416	0.4307274	0.7120258	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2395454
3121	1.3529985	0.6794579	-0.2850242	-0.0501185	0.4307274	0.7120258	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2716066
3125	-0.4654806	-0.0817113	-0.6550957	-0.7589986	0.4307274	0.7120258	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2288153
3136	-0.2749733	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0922243
3138	3.0779559	0.6794579	2.2859986	1.3676416	-0.6741149	0.7120258	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.7780313
3141	0.9823751	-0.8904536	0.4940736	-0.0501185	2.6404120	0.7120258	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4205421
3143	0.4801285	0.6794579	0.0421969	-0.0501185	0.4307274	0.7120258	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1209621
3157	0.0869906	-0.8904536	-0.2850242	-0.7589986	-0.6741149	0.7120258	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2260938
3160	-1.2517564	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.7120258	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.0988375
3169	0.9130997	0.6794579	-0.6356183	-0.7589986	0.4307274	-1.7078621	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5763531
3172	0.2272733	0.6794579	-0.2850242	-0.7589986	-0.6741149	0.7120258	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2049874
3175	-1.2604158	-1.2710382	-1.1926732	-1.1134387	0.4307274	0.7120258	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3298305
3191	1.3287521	0.6794579	0.4940736	-0.0501185	2.6404120	0.7120258	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3522463
3194	-0.3009516	0.6794579	-1.1926732	-1.1134387	-0.6741149	0.7120258	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2100286
3225	-0.0394370	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.7120258	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0562552
3227	-0.4204516	0.6794579	-0.2850242	-0.7589986	0.4307274	0.7120258	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2789237
3228	-0.5676619	-0.8904536	-1.1926732	-1.1134387	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1613400
3231	0.3623603	-0.0817113	0.0421969	-0.0501185	0.4307274	0.7120258	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1699152
3246	2.9047674	-0.0817113	2.2859986	1.3676416	-0.6741149	0.7120258	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.7569878
3254	1.1642231	-0.0817113	0.4473277	1.3676416	0.4307274	0.7120258	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2942429
3262	0.3138676	-0.0817113	-0.7836469	-0.7589986	-0.6741149	-1.7078621	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2461352
3265	-1.2517564	-0.8904536	-1.1926732	-1.1134387	-0.6741149	0.7120258	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1598387
3268	-0.8984519	0.6794579	-0.6550957	-0.7589986	-0.6741149	0.7120258	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1858747
3270	-1.0716404	-1.2710382	-0.7836469	-0.7589986	-0.6741149	0.7120258	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1425245
3274	-0.6386691	0.6794579	-0.6550957	-0.7589986	0.4307274	0.7120258	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2901861
3275	-0.5590024	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0615506
3276	-1.5981334	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.7120258	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1651991
3285	-1.4595826	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.7120258	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1584697
3293	-1.2448289	-0.8904536	-0.6550957	-0.7589986	-0.6741149	0.7120258	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1214173
3296	2.5912962	0.6794579	0.4434322	-0.0501185	0.4307274	0.7120258	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2569757
3307	-0.7252634	-0.8904536	-1.1420319	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1200933
3312	0.7381794	-0.0817113	-0.6550957	-0.0501185	-0.6741149	-1.7078621	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3354896
3314	0.1337515	-0.0817113	0.0421969	-0.0501185	0.4307274	0.7120258	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1691874
3342	-0.9175026	-1.2710382	-0.7836469	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1559525
3344	-0.9053794	-0.8904536	-0.2850242	-0.0501185	-0.6741149	0.7120258	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2564143
3349	-0.1710602	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1776246
3350	1.3287521	0.6794579	2.5197280	-0.0501185	-0.6741149	0.7120258	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.6594248
3357	-0.2039660	-1.2710382	0.0421969	-0.7589986	2.6404120	0.7120258	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3553920
3361	-1.5912058	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.7120258	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1804069
3370	1.6993755	0.6794579	-0.2850242	1.3676416	0.4307274	0.7120258	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2984295
3376	-0.4741401	-0.0817113	-1.1926732	-1.1134387	-0.6741149	0.7120258	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2134744
3377	0.9113679	-0.8904536	0.4473277	1.3676416	-0.6741149	0.7120258	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.0917908
3382	0.6186793	0.6794579	-0.6356183	-0.0501185	-0.6741149	-1.7078621	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4456807
3384	0.8334330	0.6794579	-0.6550957	-0.0501185	0.4307274	0.7120258	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2466910
3395	-0.1277631	-0.0817113	-0.7758559	-0.7589986	-0.6741149	0.7120258	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1935803
3398	-0.7789518	-0.8904536	-1.1420319	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1290414
3403	-0.1277631	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.7120258	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1180579
3404	0.1250921	0.6794579	-0.7758559	-0.7589986	0.4307274	-0.0811597	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3831775
3408	-0.9937055	-1.2710382	-1.1926732	-1.1134387	-0.6741149	-0.0811597	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3487124
3417	1.5175276	0.6794579	-0.2850242	-0.7589986	-0.6741149	-0.0811597	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4746053
3428	0.6446576	-0.8904536	0.0499878	-0.0501185	-0.6741149	-0.0811597	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2429154
3430	-0.6473286	-0.0817113	-1.1926732	-1.1134387	-0.6741149	-0.0811597	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3494584
3431	0.9910346	-0.8904536	0.4473277	1.3676416	0.4307274	-0.0811597	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2885126
3434	0.8265055	-0.8904536	-0.2850242	1.3676416	-0.6741149	-1.7078621	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.5391517
3443	1.0845564	-0.8904536	0.4473277	1.3676416	-0.6741149	-0.0811597	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1889139
3453	2.5653180	0.6794579	0.4434322	1.3676416	2.6404120	-0.0811597	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3639933
3471	-1.0872273	-0.8904536	-1.1926732	-1.1134387	0.4307274	-0.0811597	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4352986
3472	0.2896212	0.6794579	0.0266149	-0.0501185	2.6404120	-0.0811597	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.2572851
3478	0.8178461	-0.0817113	0.0499878	-0.0501185	-0.6741149	-0.0811597	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3264825
3484	0.2982806	-0.0817113	-0.7758559	-0.7589986	-0.6741149	-0.0811597	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3802177
3498	0.3104038	0.6794579	-0.5577085	-0.7589986	-0.6741149	-0.0811597	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3513579
3503	-0.0325094	-0.8904536	-0.2850242	-0.0501185	2.6404120	-0.0811597	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3040451
3504	-0.3944734	-0.8904536	-1.1926732	-1.1134387	-0.6741149	-0.0811597	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2554941
3505	0.0714037	-0.8904536	-0.6550957	-0.7589986	-0.6741149	-0.0811597	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2549209
3508	1.3529985	0.6794579	-0.6550957	1.3676416	0.4307274	-1.6406430	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5052647
3510	0.8749983	-0.8904536	-0.2850242	-0.7589986	-0.6741149	-0.0811597	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.4312437
3518	-1.5981334	-1.2710382	-1.1926732	-0.7589986	-0.6741149	-0.0811597	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3221993
3519	0.6498532	-0.0817113	-0.7836469	-0.7589986	-0.6741149	-0.0811597	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4375147
3526	0.8334330	0.6794579	0.4473277	-0.0501185	0.4307274	-0.0811597	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3607404
3531	-0.5364879	-0.8904536	-1.1420319	-0.7589986	-0.6741149	-0.0811597	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1721726
3532	-1.0196838	-0.0817113	-0.6550957	-0.7589986	0.4307274	-0.0811597	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4141531
3533	0.0454254	-1.2710382	0.0499878	-0.0501185	-0.6741149	-0.0811597	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1842177
3534	-0.5607343	-0.0817113	-0.2850242	-0.7589986	0.4307274	-0.0811597	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3244839
3542	1.1261216	0.6794579	-0.2850242	-0.7589986	-0.6741149	-0.0811597	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4093710
3547	-0.8984519	-0.8904536	-1.1420319	-0.7589986	-0.6741149	-0.0811597	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2324999
3555	0.9823751	-0.8904536	-0.6356183	-0.0501185	2.6404120	-1.6406430	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.6723580
3557	1.3460710	0.6794579	0.4473277	1.3676416	-0.6741149	-0.0811597	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3102476
3563	-1.0785679	-0.8904536	0.0266149	-0.7589986	-0.6741149	-0.0811597	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2827346
3567	1.3529985	0.6794579	0.1434796	1.3676416	0.4307274	-0.0811597	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2965916
3572	0.5649909	-0.0817113	0.0499878	-0.0501185	-0.6741149	-0.0811597	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2843400
3575	1.5227232	0.6794579	0.4473277	-0.0501185	0.4307274	-0.0811597	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3189619
3577	1.0066215	0.6794579	-0.2850242	1.3676416	0.4307274	-0.0811597	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4154493
3580	-0.6473286	-1.2710382	-0.7758559	-0.7589986	-0.6741149	-0.0811597	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1957639
3583	-0.9053794	-1.2710382	-0.7758559	-0.7589986	-0.6741149	-0.0811597	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2387723
3587	-0.7252634	-0.8904536	-1.1420319	-0.7589986	-0.6741149	-0.0811597	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2036352
3588	-1.3729883	-1.2710382	-1.1420319	-0.7589986	-0.6741149	-0.0811597	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2762349
3590	-1.0785679	-0.8904536	-1.2783740	-1.1134387	-0.6741149	-1.6406430	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3725506
3591	0.1060414	-0.0817113	-0.2850242	-0.0501185	2.6404120	-0.0811597	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2723753
3596	-0.0394370	-0.8904536	0.0499878	-0.0501185	-0.6741149	-0.0811597	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1300704
3597	0.2186139	-0.8904536	0.0499878	-0.0501185	-0.6741149	-0.0811597	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1719081
3598	0.1926356	0.6794579	-0.7836469	-0.7589986	-0.6741149	0.9405708	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2514619
3611	1.5106000	0.6794579	0.4473277	1.3676416	0.4307274	0.9405708	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2105657
3613	1.0066215	-0.0817113	-0.7135281	-0.0501185	0.4307274	0.9405708	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3919664
3616	1.5106000	0.6794579	0.4473277	1.3676416	-0.6741149	0.9405708	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2484510
3619	-0.2143573	-0.0817113	0.0421969	-0.7589986	-0.6741149	0.9405708	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3505343
3623	-1.4180174	0.6794579	-0.5577085	-0.7589986	-0.6741149	0.9405708	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3267910
3631	1.1624912	-0.8904536	-0.2850242	-0.7589986	2.6404120	0.9405708	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3567298
3633	-0.0394370	-0.8904536	-0.9472574	-0.0501185	0.4307274	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3772636
3639	2.7315789	-0.8904536	0.1512706	-0.0501185	-0.6741149	-1.6406430	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.6531265
3644	-1.1668940	-0.8904536	-1.1926732	-1.1134387	-0.6741149	0.9405708	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1631116
3648	0.3069400	0.6794579	-0.2850242	-0.0501185	0.4307274	0.9405708	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1353544
3650	-1.5912058	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.9405708	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1382076
3651	0.2186139	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.9405708	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1361837
3655	-0.7321909	-0.0817113	-0.7758559	-0.7589986	-0.6741149	0.9405708	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2084393
3662	0.1320196	-0.0817113	0.0421969	-0.0501185	-0.6741149	0.9405708	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2261462
3671	-1.4699739	-1.2710382	-1.1420319	-0.7589986	-0.6741149	0.9405708	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1493308
3679	0.3588966	-0.0817113	-0.7836469	-0.0501185	-0.6741149	0.9405708	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2866401
3681	-1.2534883	-0.8904536	-0.6745732	-0.7589986	0.4307274	0.9405708	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2636243
3689	0.5043749	-0.0817113	-0.2850242	-0.7589986	0.4307274	0.9405708	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3176254
3697	2.9047674	0.6794579	0.3382540	-0.0501185	0.4307274	-1.6406430	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5878326
3698	1.6041218	0.6794579	0.4473277	1.3676416	-0.6741149	0.9405708	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2640380
3708	0.4801285	-1.2710382	0.4473277	1.3676416	-0.6741149	0.9405708	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2462184
3721	-1.0872273	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.9405708	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1303543
3724	1.0845564	-0.8904536	0.4473277	1.3676416	-0.6741149	0.9405708	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1587463
3725	0.0714037	-1.2710382	-0.7836469	-0.7589986	2.6404120	0.9405708	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3463502
3730	1.0862882	0.6794579	0.0421969	-0.0501185	2.6404120	0.9405708	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3787709
3732	1.8639046	0.6794579	0.4473277	1.3676416	-0.6741149	0.9405708	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3073351
3736	0.3502371	-0.0817113	0.4473277	-0.0501185	-0.6741149	0.9405708	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2572984
3737	-0.4550893	0.6794579	0.0266149	-0.7589986	-0.6741149	0.9405708	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2785513
3738	-0.2212849	-1.2710382	0.0499878	-0.0501185	-0.6741149	0.9405708	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1658284
3739	0.7225924	-0.8904536	-0.6356183	-0.0501185	2.6404120	-1.6406430	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.6290609
3745	0.7381794	-1.2710382	0.4473277	1.3676416	-0.6741149	0.9405708	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2032099
3747	1.5088682	0.6794579	0.0421969	-0.0501185	-0.6741149	0.9405708	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3146131
3754	0.9113679	-1.2710382	0.4473277	1.3676416	-0.6741149	0.9405708	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1933124
3757	-1.0370027	-1.2710382	-1.1420319	-0.7589986	0.4307274	0.9405708	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3128548
3759	0.3052081	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.9405708	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1506161
3760	-0.4827995	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.9405708	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0956775
3764	0.9546650	0.6794579	-0.2850242	-0.0501185	-0.6741149	0.9405708	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2374489
3766	0.6446576	-1.2710382	0.4473277	1.3676416	-0.6741149	0.9405708	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2187969
3776	2.4960426	0.6794579	0.0421969	-0.0501185	2.6404120	0.9405708	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.2830828
3778	0.7381794	0.6794579	0.0499878	-0.0501185	-0.6741149	0.9405708	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1874635
3779	0.6602445	-0.0817113	-0.7836469	-0.0501185	-0.6741149	-1.6406430	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2905741
3782	0.7399113	0.6794579	0.0421969	-0.0501185	2.6404120	0.9405708	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4365004
3788	1.0412592	-0.0817113	-0.2850242	-0.0501185	0.4307274	0.9405708	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3263221
3791	0.6775634	-0.8904536	-0.7836469	-0.7589986	2.6404120	0.9405708	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3292076
3792	1.0066215	0.6794579	-0.2850242	1.3676416	0.4307274	0.9405708	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3262312
3793	-1.0785679	-0.8904536	-0.2850242	-0.0501185	-0.6741149	0.9405708	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3180819
3795	0.9546650	-0.0817113	-0.2850242	-0.0501185	0.4307274	0.9405708	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3118897
3811	-0.6473286	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.9405708	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1143625
3817	-1.0802998	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.9405708	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1165428
3819	0.3623603	0.6794579	0.0421969	-0.0501185	0.4307274	0.9405708	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1394249
3824	0.8178461	-0.8904536	0.4473277	1.3676416	0.4307274	0.9405708	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2416870
3826	0.9130997	0.6794579	-0.9472574	-0.0501185	-0.6741149	-1.6406430	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4386212
3832	-1.5132710	-1.2710382	-1.1926732	-1.1134387	-0.6741149	0.9405708	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1998873
3838	1.1798100	0.6794579	-0.7836469	-0.7589986	2.6404120	0.9405708	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.5311990
3839	0.4870561	-0.8904536	-0.7836469	-0.7589986	2.6404120	0.9405708	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2974564
3841	0.9996940	0.6794579	-0.2850242	-0.0501185	0.4307274	0.9405708	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2508134
3850	0.4801285	0.6794579	0.0499878	-0.0501185	-0.6741149	0.9405708	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1444550
3852	-0.7321909	-0.0817113	-0.2850242	-0.7589986	-0.6741149	0.9405708	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2902446
3860	0.0021283	-0.8904536	-0.2850242	-0.7589986	-0.6741149	0.9405708	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2500409
3867	0.1753168	-0.8904536	-0.2850242	-0.7589986	-0.6741149	0.9405708	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2789057
3868	0.9113679	0.6794579	0.0499878	-0.0501185	-0.6741149	0.9405708	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2163282
3870	0.7814765	-0.0817113	-0.2850242	-0.0501185	-0.6741149	0.9405708	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2576381
3871	1.1728825	-0.0817113	-0.2850242	1.3676416	-0.6741149	-1.6406430	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4641376
3874	1.3460710	0.6794579	0.4473277	1.3676416	-0.6741149	0.9405708	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2210295
3875	0.2186139	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.9405708	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1361837
3880	1.3460710	0.6794579	-0.2850242	1.3676416	0.4307274	0.9405708	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2776362
3881	-0.9053794	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.9405708	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1469947
3886	-0.9746548	-0.0817113	-0.2850242	-0.7589986	0.4307274	0.6044753	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2801259
3894	-0.5590024	0.6794579	0.0460923	-0.0501185	0.4307274	0.6044753	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2158144
3895	-0.9053794	0.6794579	-1.1926732	-1.1134387	-0.6741149	0.6044753	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2458838
3902	0.6775634	-0.8904536	-0.7836469	-0.7589986	2.6404120	0.6044753	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2731917
3913	-1.2517564	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.6044753	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1167626
3915	0.0817950	0.6794579	0.0460923	-0.0501185	2.6404120	0.6044753	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3474149
3921	-0.3788864	-0.8904536	-0.9472574	-0.7589986	-0.6741149	-1.6406430	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3242305
3923	-0.3788864	-1.2710382	-0.9472574	-0.7589986	-0.6741149	0.6044753	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0925994
3930	-0.3788864	0.6794579	0.1434796	-0.7589986	-0.6741149	0.6044753	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2739961
3943	-1.0785679	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.6044753	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1302198
3947	-0.5243647	-0.8904536	0.0460923	-0.0501185	0.4307274	0.6044753	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1696533
3948	0.2965487	-0.8904536	-1.1420319	-0.7589986	2.6404120	0.6044753	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2694201
3957	-0.4741401	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.6044753	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0424033
3958	0.5043749	-0.0817113	-0.2850242	-0.7589986	0.4307274	0.6044753	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2786345
3960	0.5753822	0.6794579	-0.2850242	-0.0501185	0.4307274	0.6044753	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1411038
3961	0.2186139	-0.0817113	0.0499878	-0.0501185	-0.6741149	0.6044753	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1754271
3967	-0.4741401	-0.0817113	-0.7758559	-0.7589986	-0.6741149	0.6044753	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1372084
3969	-0.0394370	-1.2710382	-0.6550957	-0.0501185	-0.6741149	-1.6406430	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3224537
3970	-0.7321909	-0.0817113	0.0266149	-0.7589986	-0.6741149	0.6044753	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3054512
3971	1.2577448	0.6794579	0.4473277	1.3676416	-0.6741149	0.6044753	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2027162
3974	0.6446576	-0.8904536	0.4473277	1.3676416	-0.6741149	0.6044753	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.0993502
3994	1.4309333	0.6794579	0.4473277	1.3676416	-0.6741149	0.6044753	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2101189
4003	0.2982806	-1.2710382	0.4473277	1.3676416	0.4307274	0.6044753	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3356961
4017	0.7381794	0.6794579	0.0499878	-0.0501185	-0.6741149	0.6044753	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1498783
4021	-0.9746548	0.6794579	-0.2850242	-0.7589986	-0.6741149	0.6044753	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2616114
4024	-0.8984519	-0.8904536	-1.1420319	-0.7589986	-0.6741149	0.6044753	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1310330
4032	-0.5001183	-0.8904536	-0.7836469	-0.7589986	-0.6741149	0.6044753	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0367751
4035	-1.4336043	-0.8904536	-1.1926732	-1.1134387	0.4307274	0.6044753	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3595051
4041	-1.0785679	-1.2710382	-0.9472574	-0.7589986	0.4307274	-1.6406430	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4556422
4043	-0.6906257	-0.8904536	-1.1420319	-0.7589986	-0.6741149	0.6044753	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0963953
4048	0.4801285	-0.8904536	0.4473277	1.3676416	-0.6741149	0.6044753	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1267717
4050	-0.0394370	-0.0817113	0.0266149	-0.0501185	0.4307274	0.6044753	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1945551
4053	1.0845564	-0.0817113	0.4473277	1.3676416	-0.6741149	0.6044753	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2150501
4054	-1.0802998	-0.8904536	-0.2850242	-0.7589986	0.4307274	0.6044753	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1812785
4057	-0.9053794	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.6044753	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1013550
4064	-1.5981334	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.6044753	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1694566
4073	0.3588966	0.6794579	-0.7836469	-0.0501185	0.4307274	0.6044753	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1881267
4075	-1.2517564	-0.8904536	-0.2850242	-0.7589986	-0.6741149	0.6044753	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1998664
4086	-0.0325094	0.6794579	-0.2850242	-0.0501185	-0.6741149	0.6044753	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1821316
4096	0.6186793	0.6794579	-0.6356183	-0.0501185	-0.6741149	-1.6406430	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4344775
4110	-1.2517564	-0.0817113	-1.1926732	-1.1134387	-0.6741149	0.6044753	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3125542
4119	-0.5070459	-0.8904536	-0.7836469	-0.7589986	0.4307274	0.6044753	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1203390
4124	-0.3858139	-0.0817113	0.0421969	-0.0501185	-0.6741149	0.6044753	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2321485
4126	-0.9157707	-1.2710382	-0.7836469	-0.7589986	-0.6741149	0.6044753	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1377388
4129	-0.4204516	-0.8904536	0.0421969	-0.0501185	-0.6741149	0.6044753	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1031311
4134	1.1624912	-0.8904536	-0.2850242	-0.7589986	2.6404120	0.6044753	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3007139
4145	-1.7713219	-1.2710382	-0.7758559	-0.7589986	-0.6741149	0.6044753	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2119890
4146	0.8178461	2.2969425	0.0499878	-0.0501185	-0.6741149	1.1422281	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2671672
4149	-0.2143573	1.1076156	-1.1926732	-1.1134387	-0.6741149	1.1422281	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3675209
4155	1.5088682	0.6794579	0.0421969	-0.0501185	-0.6741149	1.1422281	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3482226
4157	0.1337515	-1.2710382	-0.6550957	-0.0501185	-0.6741149	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3302613
4160	1.8725640	0.6794579	-0.6550957	1.3676416	0.4307274	-1.6406430	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5180150
4161	1.3027739	0.6794579	0.0266149	1.3676416	0.4307274	1.1422281	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2585422
4162	0.1337515	0.6794579	0.0421969	-0.0501185	0.4307274	1.1422281	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1723066
4165	0.5649909	1.1076156	0.0499878	-0.0501185	-0.6741149	1.1422281	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2635679
4171	-0.3788864	0.6794579	0.1434796	-0.7589986	-0.6741149	1.1422281	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3443388
4179	1.1884694	0.6794579	-0.2850242	-0.0501185	2.6404120	1.1422281	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4498871
4180	0.0021283	-0.8904536	-0.2850242	-0.7589986	-0.6741149	1.1422281	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2836505
4197	-0.4827995	-0.8904536	-0.7836469	-0.7589986	-0.6741149	1.1422281	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1292870
4201	1.5261870	2.2969425	0.4434322	1.3676416	0.4307274	1.1422281	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4122882
4208	2.7385064	0.6794579	0.4434322	-0.0501185	2.6404120	1.1422281	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4006398
4210	1.0845564	0.6794579	0.4473277	1.3676416	-0.6741149	1.1422281	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2902449
4211	0.8334330	0.6794579	-0.7797514	-0.0501185	-0.6741149	-1.6406430	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4239813
4212	1.3529985	2.2969425	0.4434322	1.3676416	0.4307274	1.1422281	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3834234
4215	1.0845564	2.2969425	0.0499878	-0.0501185	-0.6741149	1.1422281	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3116190
4216	-0.6802344	0.6794579	0.0460923	-0.0501185	0.4307274	1.1422281	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3086202
4222	-0.2212849	2.2969425	-1.1926732	-1.1134387	0.4307274	1.1422281	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.5828399
4225	1.0066215	-0.0817113	-0.2850242	-0.0501185	0.4307274	1.1422281	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3541587
4226	0.2186139	-0.0817113	0.0499878	-0.0501185	-0.6741149	1.1422281	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2466218
4234	0.6446576	-0.0817113	0.4473277	1.3676416	0.4307274	1.1422281	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.4164810
4236	-1.5132710	-1.2710382	-1.1926732	-1.1134387	-0.6741149	1.1422281	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2334969
4250	2.2189410	2.2969425	2.2859986	1.3676416	0.4307274	1.1422281	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.8348416
4253	0.7381794	1.1076156	0.0499878	-0.0501185	-0.6741149	1.1422281	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2924326
4260	-0.8620823	-1.2710382	-0.9472574	-0.7589986	-0.6741149	-1.6406430	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3578029
4264	-0.5676619	0.6794579	-0.7758559	-0.7589986	0.4307274	1.1422281	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3299253
4272	1.0412592	-0.0817113	-0.2850242	-0.0501185	0.4307274	1.1422281	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3599316
4274	0.3588966	-0.0817113	-0.7836469	-0.0501185	2.6404120	1.1422281	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3032008
4278	1.3460710	2.2969425	0.4473277	1.3676416	-0.6741149	1.1422281	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4208034
4292	2.1150279	0.6794579	-0.2850242	-0.0501185	2.6404120	1.1422281	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3077268
4296	-0.3788864	-0.8904536	-0.7836469	-0.7589986	0.4307274	1.1422281	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2199183
4304	0.0540848	0.6794579	-0.7836469	-0.7589986	-0.6741149	1.1422281	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2619796
4306	0.4714691	-0.8904536	0.4473277	1.3676416	-0.6741149	1.1422281	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2178404
4315	2.0111148	0.6794579	0.4434322	-0.0501185	0.4307274	1.1422281	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2319791
4324	0.0038602	-0.0817113	0.4473277	-0.0501185	-0.6741149	1.1422281	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3348188
4327	-0.3858139	-0.8904536	-0.6356183	-0.7589986	-0.6741149	-1.6406430	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3220861
4330	-0.3944734	2.2969425	-0.2850242	-0.7589986	0.4307274	1.1422281	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4013565
4331	-1.0785679	0.6794579	-0.2850242	-0.7589986	-0.6741149	1.1422281	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3492731
4348	0.9113679	2.2969425	0.0499878	-0.0501185	-0.6741149	1.1422281	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2827542
4349	1.1624912	-0.8904536	-0.2850242	-0.7589986	2.6404120	1.1422281	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3903394
4351	-1.5912058	-0.8904536	-0.2850242	-0.7589986	-0.6741149	1.1422281	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2549209
4352	-1.2517564	-0.0817113	-0.7758559	-0.7589986	-0.6741149	1.1422281	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2576651
4363	-0.7321909	0.6794579	-0.2850242	-0.0501185	-0.6741149	1.1422281	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3699399
4365	0.7381794	-0.0817113	0.4473277	1.3676416	-0.6741149	1.1422281	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2857084
4375	-0.9053794	-0.0817113	-0.2850242	-0.0501185	-0.6741149	1.1422281	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4478586
4379	0.4870561	0.6794579	0.4473277	-0.0501185	0.4307274	1.1422281	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2613389
4392	0.1406791	-0.0817113	-0.7836469	-0.7589986	-0.6741149	-1.6406430	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2060673
4396	-1.1928723	-0.8904536	-0.9472574	-0.7589986	-0.6741149	1.1422281	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1165887
4404	-1.0266114	-0.8904536	-0.6745732	-0.7589986	-0.6741149	1.1422281	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1772794
4406	0.1579979	-0.8904536	-1.1420319	-0.7589986	2.6404120	1.1422281	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3618274
4409	-0.3858139	-0.0817113	-0.7836469	-0.0501185	-0.6741149	1.1422281	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3391204
4419	-0.5676619	1.1076156	-1.1926732	-1.1134387	0.4307274	1.1422281	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4789084
4430	0.4714691	-0.8904536	0.4473277	1.3676416	0.4307274	1.1422281	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3330261
4436	-0.3009516	-1.2710382	0.0499878	-0.0501185	-0.6741149	1.1422281	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2127157
4441	0.0714037	-0.0817113	-0.2850242	-0.7589986	-0.6741149	1.1422281	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3517200
4446	-0.2472631	0.6794579	0.0460923	-0.0501185	0.4307274	1.1422281	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2364583
4448	-1.0716404	-0.8904536	-0.7914378	-0.7589986	-0.6741149	1.5724304	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2219975
4449	0.5667228	0.6794579	-0.6356183	-0.0501185	0.4307274	-1.6406430	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5047327
4450	1.5781436	0.6794579	0.4434322	-0.0501185	-0.6741149	1.5724304	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.4446749
4458	0.7052736	0.6794579	0.4551187	-0.0501185	2.6404120	1.5724304	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.5307415
4459	-0.7321909	1.1076156	-1.1926732	-1.1134387	-0.6741149	1.5724304	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4304218
4463	-0.7252634	0.6794579	-0.7836469	-0.7589986	-0.6741149	1.5724304	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2789856
4465	-0.9157707	-1.2710382	-0.7836469	-0.7589986	-0.6741149	1.5724304	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2629419
4471	-0.5676619	-1.2710382	0.0499878	-0.0501185	0.4307274	1.5724304	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4022754
4477	-0.9053794	-0.0817113	-0.2850242	-0.0501185	-0.6741149	1.5724304	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.5195590
4478	-0.0325094	2.2969425	-0.2850242	-0.0501185	-0.6741149	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3226262
4483	-0.3269299	-0.8904536	0.0266149	-0.0501185	-0.6741149	1.5724304	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2514670
4486	-0.5676619	-1.2710382	0.0499878	-0.0501185	-0.6741149	1.5724304	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3288678
4491	0.1164327	-0.0817113	-0.6356183	-0.0501185	-0.6741149	-1.5330924	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2059831
4493	-0.3788864	0.6794579	-0.2850242	-0.0501185	0.4307274	1.5724304	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3346129
4513	0.8265055	0.6794579	0.4473277	-0.0501185	0.4307274	1.5724304	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3896141
4523	0.3138676	0.6794579	-0.7836469	-0.7589986	0.4307274	1.5724304	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.4057067
4525	0.3918024	-1.2710382	0.4473277	1.3676416	-0.6741149	1.5724304	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3662493
4529	2.7385064	0.6794579	0.4434322	-0.0501185	2.6404120	1.5724304	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4723402
4531	-1.2517564	-0.0817113	-0.7758559	-0.7589986	-0.6741149	1.5724304	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3293655
4537	-1.0785679	0.6794579	-1.1926732	-1.1134387	-0.6741149	1.5724304	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4167917
4542	0.2272733	2.2969425	0.0266149	-0.0501185	0.4307274	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3318362
4543	-0.1277631	-0.8904536	0.0499878	-0.0501185	-0.6741149	1.5724304	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2143770
4544	-0.0394370	2.2969425	-0.7758559	-0.7589986	-0.6741149	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4426320
4549	2.7315789	-0.8904536	0.1512706	-0.0501185	-0.6741149	-1.5330924	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.6352014
4555	1.0862882	2.2969425	0.4940736	-0.0501185	2.6404120	1.5724304	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.7828337
4558	2.2189410	2.2969425	2.2859986	1.3676416	0.4307274	1.5724304	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.9065419
4563	-1.2171187	-0.8904536	-0.7914378	-0.7589986	-0.6741149	1.5724304	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1977511
4572	0.5892373	2.2969425	0.0460923	-0.0501185	-0.6741149	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3001169
4574	0.3485053	-0.8904536	-0.7836469	-0.7589986	2.6404120	1.5724304	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3796746
4575	0.5667228	2.2969425	0.4551187	-0.0501185	0.4307274	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4175930
4577	0.2792299	2.2969425	0.0266149	-0.0501185	-0.6741149	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2701194
4579	-1.0802998	-0.8904536	-0.2850242	-0.7589986	0.4307274	1.5724304	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3426043
4587	-0.3009516	-1.2710382	0.0499878	-0.0501185	-0.6741149	1.5724304	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2844161
4594	-0.5676619	-1.2710382	0.4473277	-0.0501185	-0.6741149	1.5724304	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3948952
4597	0.6602445	-0.0817113	-0.7836469	-0.0501185	-0.6741149	-1.5330924	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2726490
4606	0.4783966	-1.2710382	0.4473277	1.3676416	-0.6741149	1.5724304	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3518170
4609	1.3356797	0.6794579	0.0421969	-0.0501185	-0.6741149	1.5724304	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3910583
4610	0.2168820	0.6794579	0.0499878	-0.0501185	-0.6741149	1.5724304	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2695569
4615	-0.0671471	0.6794579	0.0460923	-0.0501185	-0.6741149	1.5724304	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3162459
4616	-1.0785679	-0.8904536	-0.7758559	-0.7589986	-0.6741149	1.5724304	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2234399
4618	1.5781436	2.2969425	0.4434322	1.3676416	0.4307274	1.5724304	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4926480
4634	-1.0266114	-0.8904536	-0.6745732	-0.7589986	-0.6741149	1.5724304	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2489798
4637	-0.5070459	-0.8904536	-0.2850242	-0.7589986	2.6404120	1.5724304	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4313385
4642	1.5106000	2.2969425	0.4473277	1.3676416	0.4307274	1.5724304	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4820400
4646	-0.0498283	-0.8904536	-0.7836469	-0.7589986	0.4307274	1.5724304	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3464617
4656	1.1728825	-0.0817113	-0.2850242	1.3676416	-0.6741149	-1.5330924	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4462125
4659	0.9096360	2.2969425	0.0499878	-0.0501185	-0.6741149	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3541659
4660	-1.4180174	-0.8904536	-0.7914378	-0.7589986	-0.6741149	1.5724304	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2133543
4665	-0.3858139	1.1076156	-0.7758559	-0.7589986	-0.6741149	1.5724304	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3330217
4668	1.0932158	0.6794579	0.4434322	1.3676416	0.4307274	1.5724304	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3219675
4669	-0.8118576	-0.8904536	-0.7836469	-0.7589986	-0.6741149	1.5724304	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2457954
4677	-0.3944734	-0.0817113	0.0266149	-0.7589986	-0.6741149	1.5724304	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4262745
4678	0.6879547	2.2969425	-0.2850242	-0.0501185	2.6404120	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.7352120
4681	0.7745490	2.2969425	0.4940736	-0.0501185	0.4307274	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4587231
4684	0.0991138	0.6794579	0.4551187	-0.0501185	0.4307274	1.5724304	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3186003
4686	-0.0394370	0.6794579	0.0499878	-0.0501185	-0.6741149	1.5724304	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3122768
4688	0.8334330	-0.0817113	-0.7836469	-0.0501185	-0.6741149	-1.7213059	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3328827
4689	-0.0394370	0.6794579	-0.9472574	-0.7589986	-0.6741149	-1.5330924	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3467538
4707	1.0412592	-0.0817113	-0.2850242	-0.0501185	0.4307274	1.5724304	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.4316320
4711	-0.4377705	2.2969425	-0.7914378	-0.7589986	-0.6741149	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.5116179
4716	-0.2212849	2.2969425	-1.1926732	-1.1134387	0.4307274	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.6545403
4717	-0.0480964	-0.0817113	0.4473277	-0.0501185	-0.6741149	1.5724304	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.4019170
4726	-0.8534229	-0.8904536	-0.2850242	-0.7589986	2.6404120	1.5724304	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4890680
4728	-0.8205171	0.6794579	-1.1926732	-1.1134387	-0.6741149	1.5724304	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3737832
4732	0.7087373	2.2969425	0.4940736	-0.0501185	0.4307274	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4477545
4734	0.1250921	0.6794579	0.4473277	-0.0501185	-0.6741149	1.5724304	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3510786
4736	-1.0785679	0.6794579	-0.7758559	-0.7589986	-0.6741149	1.5724304	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3391682
4748	1.7513320	0.6794579	0.4434322	-0.0501185	-0.6741149	1.5724304	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.4158101
4749	-0.9140388	-0.8904536	-1.2783740	-1.1134387	-0.6741149	-1.5330924	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3820470
4752	0.9996940	1.1076156	0.4473277	1.3676416	-0.6741149	1.5724304	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3440322
4753	-0.3858139	-0.8904536	0.0460923	-0.0501185	0.4307274	1.5724304	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3368919
4757	-1.4249449	-0.8904536	-0.7758559	-0.7589986	-0.6741149	1.5724304	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2171059
4758	0.1753168	-0.8904536	-0.2850242	-0.7589986	-0.6741149	1.5724304	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3842156
4769	1.2317666	0.6794579	0.4434322	1.3676416	0.4307274	1.5724304	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2988757
4787	-0.9140388	0.6794579	-1.1926732	-1.1134387	-0.6741149	1.5724304	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3893702
4788	1.1278535	0.6794579	-0.2850242	-0.0501185	-0.6741149	1.5724304	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3716236
4790	-0.5849807	2.2969425	-0.7914378	-0.7589986	0.4307274	1.5724304	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.5892104
4793	-0.7408504	-0.0817113	-0.7758559	-0.7589986	0.4307274	1.5724304	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4357887
4796	-0.2212849	-0.8904536	0.0499878	-0.0501185	0.4307274	1.5724304	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3649627
4800	1.5106000	0.6794579	-0.2850242	1.3676416	-0.6741149	-1.5330924	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3390723
4801	-0.5607343	-0.0817113	0.0266149	-0.7589986	0.4307274	1.5724304	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4244049
4805	-0.1294950	-0.8904536	0.0499878	-0.0501185	-0.6741149	1.5724304	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2146656
4810	-0.0480964	2.2969425	-0.7758559	-0.7589986	-0.6741149	0.6851382	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3402084
4813	-0.3788864	-0.8904536	0.4434322	-0.7589986	-0.6741149	0.6851382	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2678298
4817	-1.4526551	-0.8904536	-0.2850242	-0.7589986	-0.6741149	0.6851382	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2135717
4825	0.0436935	0.6794579	0.0499878	-0.0501185	-0.6741149	0.6851382	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1505396
4832	-0.2160892	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.6851382	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0812160
4841	-1.0266114	-0.8904536	-0.6745732	-0.7589986	-0.6741149	0.6851382	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1518846
4842	-0.9071113	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.6851382	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1150875
4843	0.2203458	-0.8904536	0.4473277	1.3676416	-0.6741149	0.6851382	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1835126
4845	0.6602445	-0.8904536	-0.6745732	-0.7589986	0.4307274	-1.5330924	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4203032
4846	0.8265055	0.6794579	0.4940736	1.3676416	0.4307274	0.6851382	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2310513
4848	0.3069400	2.2969425	0.0499878	-0.0501185	-0.6741149	0.6851382	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.1655297
4849	-0.2645820	-0.8904536	0.4940736	-0.0501185	0.4307274	0.6851382	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2838787
4853	-0.2160892	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.6851382	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0812160
4858	1.0066215	0.6794579	0.4434322	1.3676416	0.4307274	0.6851382	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.1925917
4859	1.5192595	0.6794579	2.2859986	2.7854017	-0.6741149	0.6851382	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4310888
4862	0.4870561	0.6794579	-0.2850242	-0.7589986	-0.6741149	0.6851382	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1836006
4864	-0.3078791	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.6851382	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0965143
4865	1.3529985	2.2969425	0.4473277	1.3676416	2.6404120	0.6851382	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.5647056
4878	-0.8984519	-0.0817113	-0.2850242	-0.0501185	0.4307274	0.6851382	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3067138
4886	-0.0394370	-0.0817113	-1.1030770	-0.7589986	-0.6741149	-1.5330924	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2458622
4887	-1.0716404	-0.8904536	-0.7914378	-0.7589986	-0.6741149	0.6851382	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1320396
4897	-1.5912058	-0.8904536	-0.7914378	-0.7589986	-0.6741149	0.6851382	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1522612
4907	-0.1260312	0.6794579	0.4940736	-0.0501185	0.4307274	0.6851382	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2147349
4916	1.2594767	-0.8904536	2.2859986	2.7854017	-0.6741149	0.6851382	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.5028851
4919	0.5840416	0.6794579	0.4940736	-0.0501185	0.4307274	0.6851382	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2091124
4928	0.3623603	0.6794579	0.4940736	-0.0501185	0.4307274	0.6851382	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1721656
4938	0.9113679	0.6794579	0.4473277	1.3676416	-0.6741149	0.6851382	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2470019
4947	-0.1537414	0.6794579	0.0266149	-0.0501185	-0.6741149	0.6851382	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1795500
4958	0.4870561	2.2969425	0.0577788	1.3676416	0.4307274	0.6851382	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4705184
4959	0.9996940	2.2969425	0.4473277	1.3676416	-0.6741149	0.6851382	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3984452
4964	0.6533170	-0.0817113	-0.6356183	-0.0501185	0.4307274	-1.5330924	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3937154
4974	-0.0325094	0.6794579	0.0266149	-0.0501185	-0.6741149	0.6851382	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1593446
4976	-0.9919737	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.6851382	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1292312
4989	-0.8187852	0.6794579	0.0460923	-0.0501185	0.4307274	0.6851382	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2555303
4999	0.8143823	-0.8904536	-0.7836469	-0.7589986	2.6404120	0.6851382	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3094387
5005	0.3848748	-0.8904536	0.4473277	1.3676416	-0.6741149	0.6851382	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1560911
5007	1.2560130	-0.8904536	2.2859986	2.7854017	-0.6741149	0.6851382	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.5034623
5017	0.1406791	0.6794579	0.0577788	1.3676416	0.4307274	0.6851382	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3338607
5021	0.7225924	0.6794579	2.5976378	-0.0501185	0.4307274	0.6851382	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.5827983
5022	-0.0480964	0.6794579	0.0499878	-0.0501185	0.4307274	0.6851382	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1277315
5029	-1.0785679	0.6794579	-0.7758559	-0.7589986	-0.6741149	0.6851382	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1912861
5033	0.5147662	-0.8904536	-0.6356183	-0.0501185	0.4307274	-1.5330924	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4004619
5044	1.4309333	-0.8904536	2.2859986	2.7854017	-0.6741149	0.6851382	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4743090
5048	0.4714691	2.2969425	0.4473277	-0.0501185	-0.6741149	0.6851382	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2434946
5057	-0.7339228	-0.8904536	-0.2850242	-0.7589986	-0.6741149	0.6851382	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1680280
5060	-1.2448289	-0.8904536	-0.7914378	-0.7589986	0.4307274	0.6851382	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2580452
5064	0.6931504	0.6794579	0.0577788	1.3676416	2.6404120	0.6851382	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3811214
5084	0.5494039	-0.8904536	2.5976378	-0.0501185	0.4307274	0.6851382	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.7508350
5096	0.0869906	-0.8904536	-0.7836469	-0.7589986	0.4307274	0.6851382	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2213829
5106	-1.4180174	-0.0817113	-0.2850242	-0.7589986	-0.6741149	0.6851382	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3425891
5110	-0.4758720	-0.8904536	0.0499878	-0.0501185	-0.6741149	0.6851382	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1245131
5113	-0.1727921	-0.0817113	-0.6356183	-0.7589986	2.6404120	0.6851382	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2477972
5116	-0.2472631	0.6794579	-0.9472574	-0.7589986	-0.6741149	-1.5330924	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3813915
5129	-1.4249449	-0.8904536	-0.7758559	-0.7589986	-0.6741149	0.6851382	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1271480
5130	0.4524184	2.2969425	0.0266149	-0.0501185	0.4307274	0.6851382	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2232581

hist(sorted_df$diff, breaks = 20, col = "blue", main = "Mean Absolute Difference", xlab = "Difference")

Figure 22: Mean Absolute Difference

muHVT: Predicting Cells with Layers using predictLayerHVT

Zubin Dowlaty, Srinivasan Sudarsanam, Somya Shambhawi

2023-06-23

1 Abstract

2 Example: muHVT with the Personal Computer dataset

3 Map A : Base Compressed Map

3.0.1 Iteration 1:

3.0.2 Iteration 2:

4 Map B : Compressed Novelty Map

4.1 Voronoi Tessellation with highlighted novelty cell

5 Map C : Compressed Map without Novelty

5.0.1 Iteration 1:

5.0.2 Iteration 2:

6 Prediction on Test Data

7 Executive Summary

8 References